On Retaining Intermediate Probabilistic Models When Building Bayesian Networks

合集下载

GRE 1.19

GRE 1.19

1.19Sec.11.Among the M people of DNG,legends are associated with specific S region,andthese legends are________;only the cave owner can share its secretsA.impenetrableB.immutableC.proprietaryD.didacticE.self-perpetuating2.It is paradox of the V ictorims that they were both_____________and,through their empire, cosmopolitanA.capriciousB.insularC.mercenaryD.idealisticE.intransigent3.Despite the scathing precision with which she satirizes the lives of social aspirants and moneyed folk,the writer appears to______being part of the world she presents as so________A.abhor D.unattainableB.relish E.insufferableC.evoke F.enchanting4.The contemporary trend whereby fashion designers flout mainstream tradition is unique only in its___earlier fashion designers experience the same____impulse,albeit in a less extreme form.A.subversiveness D.indiscriminateB.intensity E.iconoclasticC.culpability F.temperate5.Memory-prompt technology such as online birthday remainders does more than enhance our recall abilities;it induce us to____over more behaviors to automated processes.Witness the____ a program that allows us to create computer greeting cards for the entire year in one sitting.A.delegate D.controversy overB.ascribe E.popularity ofC.liken F.sophistication of6.Biologists have little_____drawing the link between the success of humanity and human____. Indeed,many biologists claim that this attributes,the ability to____or to put it more sharply,to make individuals subordinate their self-interest to the needs of the group,lies at the root of human achievement.A.consensus regarding D.resilience G.reflectpunction about E.sociability municateC.justification for F.uniqueness I.CooperateMany cultural anthropologists have come to reject the scientific framework of empiricism that dominated the field until the1970s and now regard all scientific knowledge as sociality constructed.They argue that information about culture during the empiricist era typically came from anthropologist who brought with them a prepacked set of conscious and unconscious biases. Cultural anthropology,according to the post-1970s critique,is unavoidably subjective,and the anthropologist should be explicit in acknowledging that fact.Anthropology should stop striving to build a better database about cultural behavior and should turn to developing a more humanistic interpretation of cultures.The new frame work holds that it may be more enlightening to investigate the biases of earlier texts than to continue with empirical methodologies.7.The author implies which of the following about most cultural anthropologists working prior to the1979?C.They regarded scientific knowledge as consisting of empirical truths8.According to the passage,“many cultural anthropologists”today would agree that anthropologists shouldD.turn to examining older anthropological texts for unacknowledged biases.Despite winning several prestigious literary awards of the day,when it first appeared,Alice Walker’s The Color Purple generated critical unease over puzzling aspects of its compositions.In what,as one reviewer put it,was“clearly intended to be a realistic novel,”many reviewers perceived violations of the conventions of the realistic novel form,pointing out variously that late in the book,the narrator protagonist Celie and her friends are propelled toward a happy ending with more velocity than credibility,that the letters from Nettie to her sister Celie intrude into the middle of the main action with little motivation or warrant,and that the device of Celie’s letters to God is especially unrealistic inasmuch as it forgoes the concretizing details that traditionally have given the epistolary novel(that is,a novel composed of letters)its peculiar verisimilitude:the ruses to enable mailing letters,the cache,and especially the letters received in return.Indeed,the violations of realistic convention are so flagrant that they might well call into question whether The Color of Purple is indeed intended to be a realistic novel,especially since there are indications that at least some of those aspects of the novel regarded by viewers as puzzling may constitutes its links to modes of writing other than Anglo-European nineteenth-century realism.For example,Henry Louis Gates,Jr.,has recently located the letters to God within an African American tradition deriving from slave narrative,a tradition in which the act of writing is linked to a powerful deity who“speaks”through scripture and bestows literacy as an act of grace.For Gates,the concern with finding a voice,which he sees as the defining feature of African American literature,links Celie’s letters with certain narrative aspects of Zora Neale Hurston’s1937novel Their Eyes W ere W atching God,the acknowledged predecessor of The Color Purple.Gates’s paradigm suggests how misleading it may be to assume that mainstream realist criteria are appropriate for evaluating The Color Purple.But in his preoccupation with voice as a primary element unifying both the speaking subject and the text as a whole Gates does not elucidate manyof the more conventional structural features of Walker’s novel.For instance,while the letters from Nettie clearly illustrate Nettie’s acquisition of her own voice,Gates’s focus on“voice”sheds little light on the place that these letters occupy in the narrative or on why the plot takes this sudden jump into geographically and culturally removed surroundings.What is needed is an evaluative paradigm that,rather than obscuring such startling structural features(which may actually be explicitly intended to undermine traditional Anglo-European novelistic conventions),confronts them,thus illuminating the deliberately provocative ways in which The Color Purple departs from the traditional models to which it has been compared.9.The author of the passage would be most likely to agree with which of the following statements about the letters from Nettie to Celie?A.They mark an unintended shift to geographically and culturally removed surroundingsB.They may represent a conscious attempt to undermine certain novelistic conventionsC.They are more closely connected to the main action of the novel than is at first apparentD.They owe more to the tradition of the slave narrative than do Celie’s letters to GodE.They illustrate the traditional concretizing details of the epistolary novel form10.In the second paragraph,the author of the passage is primarily concerned withA.examining the ways in which The Color Purple echoes its acknowledged predecessor,Their Eyes W ere W atching GodB.providing an example of a critic who has adequately addressed the structural features of The Color PurpleC.suggesting that literary models other than the nineteenth-century realistic novel may inform our understanding of The Color PurpleD.demonstrating the ineffectiveness of a particularly scholarly attempt to suggest an alternative way of evaluating The Color PurpleE.disputing the perceived notion that The Color Purple departs from conventions of the realistic novel form11.According to the passage,an evaluative paradigm that confronts the startling structural features of The Color Purple would accomplish which of the following?A.It would adequately explain why many reviewers of this novel have discerned its connections to the realistic novel traditionB.It would show the ways in which this novel differs from its reputed Anglo-European nineteenth-century modelsC.It would explicate the overarching role of voice in this novelD.It would address the ways in which this novel echoes the central themes of Hurston’s Their Eyes Are W atching GodE.It would reveals ways in which these structural features serve to parody novelistic conventions12.The author of the passage suggests that Gates is most like the reviewers mentioned in the first paragraph in which of the following ways?A.He points out discrepancies between The Color Purple and other traditional epistolary novelsB.He sees the concern with finding a voice as central to both The Color Purple and Their EyesAre W atching GodC.He assumes that The Color Purple is intended to be a novel primarily in the tradition of Anglo-American nineteenth-century realismD.He does not address many of the unsettling structural features of The Color PurpleE.He recognizes the departure of The Color Purple from traditional Anglo-European realistic novel conventions.13.Progressive and reactionary populist movements are not necessarily_____________;each may and usually does,possess features of the other.A.dichotomiesB.untenableC.unsustainableD.contradictoryE.subversiveF.efficacious14.Flawed as it may be because it is conducted by subjective scientists,science itself has methods that help us_____our biases and talk about objective reality with some validityA.bypassB.reduceC.exacerbateD.magnifyE.acknowledgeF.circumvent15.In Japanese aesthetics,especially but not only in MH,beauty contains the idea of ___________;beauty must have an air of evanescence,the intimation of its own demiseA.transienceB.symmetryC.decayD.simplicityE.balanceF deterioration16.Although one can adduce myriad of examples of ecosystem disruption by nonindigenous species,nevertheless most introduced species that survive in fact appear to have quite_ ____effects on the ecosystem they have invadedA.minimalB.triflingC.markedD.conspicuousE.intriguingF.deleterious(The Great Sphinx is a huge statue in Egypt….In over10000years胡夫是2600B.C的腿是1万年前的)脸非胡夫17.削弱:E.The face of the Sphinx is small relative to the rest of the head,indicating that the face may have been recarved long after the Sphinx was builtAs of the late1980’s,neither theorists nor large-scale computer climate models could accurately predict whether cloud systems would help or hurt a warming globe.Some studies suggested that a four percent increase in stratocumulus clouds over the ocean could compensate for a doubling in atmospheric carbon dioxide,preventing a potentially disastrous planetwide temperature increase. On the other hand,an increase in cirrus clouds could increase global warming.That clouds represented the weakest element in climate models was illustrated by a study of fourteen such paring climate forecasts for a world with double the current amount of carbon dioxide,researchers found that the models agreed quite well if clouds were not included. But when clouds were incorporated,a wide range of forecasts was produced.With such discrepancies plaguing the models,scientists could not easily predict how quickly the world’s climate would change,nor could they tell which regions would face dustier droughts or deadlier monsoons.18.The author of the passage is primarily concerned withA.confirming a theoryB.supporting a statementC.presenting new informationD.predicting future discoveriesE.reconciling discrepant findings19.It can be inferred that one reason the14models described in the passage failed to agree was thatA.they failed to incorporate the most up-to-date information about the effect of clouds on climateB.they were based on faulty information about factors other than clouds that affect climateC.they were based on different assumptions about the overall effects of clouds on climateD.their originators disagreed about the kinds of forecasts the models should provideE.their originators disagreed about the factors other than clouds that should be included in the models20.The information in the passage suggests that scientists would have to answer which of the following questions in order to predict the effect of clouds on the warming of the globe?A.What kinds of cloud systems will form over the Earth?B.How can cloud systems be encouraged to form over the ocean?C.What are the causes of the projected planetwide temperature increase?D.What proportion of cloud systems are currently composed of cirrus of clouds?E.What proportion of the clouds in the atmosphere form over land masses?21.It can be inferred that the primary purpose of the models included in the study discussed in the second paragraph of the passage was to(A)predict future changes in the world’s climate(B)predict the effects of cloud systems on the world’s climate(C)find a way to prevent a disastrous planetwide temperature increase(D)assess the percentage of the Earth’s surface covered by cloud systems(E)estimate by how much the amount of carbon dioxide in the Earth’s atmosphere will increaseSec.21.Apparently,advanced tortoises evolved multiple times:the high-domed shells and columnar, elephantine feet of forms are specializations for terrestrial life that evolved____on each continent.A.independentlyB.interchangeablyC.paradoxicallyD.simultaneouslyE.symmetrically2.Instead of demonstrating the____of archaeological applications of electronic remote sensing, the pioneering study became,to some skeptics,an illustration of the imprudence of interpreting sites based on virtual archaeology.A.ubiquityB.limitationC.promiseD.redundancyplexity3.Given the___the committees and the____nature of its investigation,it would be unreasonable to gainsay the committee’s conclusions at first glance.A.sterling reputation of D.superficialck of finding of E.spontaneousC.ad hoc existence of F.exhaustive4.Though many professional book reviewers would agree that criticism should be_____enterprise,a tendency to write____reviews has risen;partly out of the mistaken belief that sharing personal details will help reviewers stand out of the pack.A.anonymous D.scathingB.an evenhanded E.confessionalC.a spirited F.superficial5.Scientific papers often____what actually happened in the course of the investigations they describe.Misunderstandings,blind alleys,and mistakes of various sorts will fail to appear in the final written accounts because____is a desirable attribute when transmitting results in a scientificreport and served by____.A.amplify D.transparency G.a comprehensive historical accountB.misrepresent E.efficiency H.a purely quantitative analysisC.particularize F.exhaustiveness I.an overly superficial discussion6.Analysis of475-million-year-old fossils from Pakistan has yielded fresh insights into the early ancestors of modern whales.For example,M I was a land animal----life in the sea.One M.Innus fossil encased a fetus positioned for a head-first delivery which is typical of a land mammal and suggests the species gave birth onshore.But it probably spent much of its time___:its big teeth were suited for catching fish,while its flipper-like feet must have been__walking.A.resistant D.in the water G.incompatible withB.removed from E.fleeing from predators H.clumsy forC.adapted to F.protecting its young I.strengthened byThe editors of the essay collection RB tells us repeatedly that biography is an invention of the Romantic period in British literature(late eighteen and nineteen centauries),yet we are never shown that processes of invention in motion.H,the most prominent example of the Romantic biographer,is almost invisible.The Romantic period was not just the period in which biography was invented---or,rather,the period in which some of its informing principles were invented, since biography could just as easily be said to have originated in the scandalous memoirs that formed part of the pre-Romantic culture of the novel.It was also the period in which biography, through its sheer ubiquity,became an object of major ideological significance within British culture.7.The passage mentions the“scandalous memoirs”that were written prior to the Romantic period primarily in order toA.indicate an alternative account of the origins of biography8.According to the passage,biography attained great significance within British culture during the Romantic period because biographiesC.were so widespread in Britain at the time(Our study revealed that nest-guarding long-tailed….even if the nest may have already been preyed upon)skinksS离家近+S回家更成功Reason1:S离家太远→S回不了家转:R1错↑无论多远都有S能找到家Reason2:trade-off代价小→花能量回家远→花能量多回家远→蛋被吃←snake蛋多→更多回家9.The primary purpose of the passage is toB.consider explanation for a finding regarding long-tailed skinks10.The claim in the highlighted sentence assumes which of the following about the individuals that managed to find their way home from each distance?D.They did not possess better homing skills than did the other long-tailed skinks studied11.The“second possibility”implies which of the following as a possible explanations for the female long-tailed skinks that failed to home from distances over50meters?A.They had relatively small churches12.The C.P archaeological site was initially interpreted as indicative of_____society,since it was thought to have been sat the center of a cluster of smaller,contemporary settlements that itpresumably controlled.A.an expansionistB.a hierarchicalC.an urbanD.heterogeneousE.a diverseF.stratified12.The CP archaeological site was initially interpreted as indicative of_____society,since it was thought to have been at the center of a Chester of smaller,contemporary settlements that it presumably controlled.A.an expansionistB.a hierarchicalC an urbanD.heterogeneousE.a diverseF.stratified13.Even if the story now seems a surprisingly innocuous overture to the author’s later,more fully developed narrations,it____some of the key traits of those bleaker tales.A.avoidsB.beliesC.undercutsD.anticipatesE.possessesF prefigures14.In the absence of a surface gradient.The new laws of refraction and refraction are theconventional law,so they represent more of an extension than a completeA.inferable fromB.entailed byC.antithetical toD.coincident toE.antecedent toF.oppositional to15.While recognizes that recent reports of cyberwarfare-phone-hacking scandals,and identity thefts have tended to accent the destruction connotation of the world.Sye.H maintains that”Hacking”is such term that is meaning nearly always derives from its context.A.a genericB.an inclusiveC.positiveD.subjectiveE.affirmativeF.technical(stylistic evidence….Reached the Americas)画里有犰狳+VE活的时候欧洲没有犰狳↓画不是VE画的16.In the argument given,the two highlighted portions play which of the following roles?C.The first presents the main conclusion of the argument;the second provides evidence in support of that conclusion.[From1910to1913,….opposition from anti-suffragists]女人游街(男人游→solidarity)↓↓solidarity~social order social order游街不女人Scholars:让步——游街→social changeScholars转:L:游街——双刃剑↑PC+opposition17.It can be inferred from the passage that men’s and women’s parades were similar in that bothE were intended by their participants as public declarations of solidarity18.The passage suggests which of the following about proponents of the“rules of social order”D.They believed that it was unfeminine for women to march in suffrage parades.[Some attine ants carry…….ant hosts]Ants抗植被→种花园→产蘑菇吃Ants:active→(看似)蚂蚁控制蘑菇转:1.共生久→cultivar traits→benefit蘑菇2.很多微生物产生机理→控制共生动物1+2↓很可能蘑菇控制蚂蚁19.The passage points out which of the following in order to explain the appeal of the notion that some attine ants cultivate and control fungus?A.The ants play the behaviorally active roles in the symbiotic relationship20.In the context in which it appears,the word“manipulate”most nearly means.E.outmaneuver答案:S1S21C1A2B2C3BE3CF4BE4AE5AF5BEG6BEI6CDH9B12BF10C13DF11B14AB12E15AB13AD14AF15CF16AB18B19C20A21A。

高二英语数学建模方法单选题20题

高二英语数学建模方法单选题20题

高二英语数学建模方法单选题20题1.In the process of mathematical modeling, the factor that determines the outcome is called_____.A.independent variableB.dependent variableC.control variableD.extraneous variable答案:B。

本题考查数学建模中的基本术语。

独立变量(independent variable)是指在实验或研究中被研究者主动操纵的变量;因变量dependent variable)是指随着独立变量的变化而变化的变量,在数学建模中决定结果的因素通常是因变量;控制变量(control variable)是指在实验中保持不变的变量;无关变量(extraneous variable)是指与研究目的无关,但可能会影响研究结果的变量。

2.The statement “The value of y depends on the value of x” can be represented by a mathematical model where y is the_____.A.independent variableB.dependent variableC.control variableD.extraneous variable答案:B。

在“y 的值取决于x 的值”这句话中,y 是随着x 的变化而变化的变量,所以y 是因变量。

3.In a mathematical model, the variable that is held constant toobserve the effect on other variables is_____.A.independent variableB.dependent variableC.control variableD.extraneous variable答案:C。

2022年考研考博-考博英语-昆明理工大学考试全真模拟易错、难点剖析AB卷(带答案)试题号:67

2022年考研考博-考博英语-昆明理工大学考试全真模拟易错、难点剖析AB卷(带答案)试题号:67

2022年考研考博-考博英语-昆明理工大学考试全真模拟易错、难点剖析AB卷(带答案)一.综合题(共15题)1.单选题Does using a word processor affect a writer’s style? The medium usually does do something to the message after all, even if Marshall McLuhan’s claim that the medium simply is the message has been heard and largely forgotten now. The question matters. Ray Hammond, in his excellent guide The Writer and the Word Processor, predicts that over half the professional writers in Britain and the USA will be using word processors by the end of 1985. The best-known recruit is Len Deighton, from as long ago as 1968, though most users have only started since the microcomputer boom began in 1980.Ironically word processing is in some ways psychologically more like writing in rough than typing, since it restores fluidity and provisionally to the text. The typist’s dread of having to get out the tippex, the scissors and paste, or of redoing the whole thing if he has any substantial second thoughts, can make him consistently choose the safer option in his sentences, or let something stand which he knows to be unsatisfactory or incomplete, out of weariness. In word processing the text is loosened up whilst still retaining the advantage of looking formally finished.This has, I think, two apparently contradictory effects. The initial writing can become excessively sloppy and careless, in the expectation that it will be corrected later. That crucial first inspiration is never easy to recapture, though, and therefore, on the other hand, the writing can become over-deliberated, lacking in flow and spontaneity, since revision becomes a large part of composition. However, these are faults easier to detect in others than in oneself. My own experience of the sheer difficulty of committing any words at all to the page means I’m grateful for all the help I can get.For most writers, word processing quite rapidly comes to feel like the ideal method (and can always be a second step after drafting on paper if you prefer). Most of the writers interviewed by Hammond say it has improved their style, (“immensely”,says Deighton). Seeing your own word on a screen helps you to feel cool and detached about them.Thus it is not just by freeing you from the labor of mechanical retyping that a word processor can help you to write. One author (Terence Feely) claims it has increased his output by 400%. Possibly the feeling of having a reactive machine, which appears to do things, rather than just have things done with it, accounts for this—your slave works hard and so do you.Are there no drawbacks? It costs a lot and takes time to learn—“expect to lose week s of work”,says Hammond, though days might be nearer the mark. Notoriously it is possible to lose work altogether on a word processor, and this happens to everybody at least once. The awareness that what you have written no longer exists anywhere at all, is unbelievably enraging and baffling.Will word processing generally raise the level of professional writing then? Does it make writers better as well as more productive? Though all users insist it has done so for them individually, this is hard to believe. But reliance happens fast. 1.What appears to be changing rapidly in Britain and the USA?2.Typing a manuscript in the conventional manner may make a writer().3.One effect of using a word processor may be that the ongoing revision of a text().4.It is claimed here that word processors create().5.As far as learning to use a word processor is concerned, the author of the passage feels that Hammond().问题1选项A.The style writers are employing.B.The way new writers are being recruited.C.The medium authors are using.D.The message authors are putting forward.问题2选项A.have a lot of second thoughtsB.become overcritical of his or her workC.make more mistakesD.take few risks问题3选项A.is done with too little attentionB.produces a sloppy effectC.fails to produce a fluent styleD.does not encourage one to pick up mistakes问题4选项A.a feeling of distance between a writer and his or her workB.the illusion that you are the servant of the machineC.a sensation of powerD.a reluctance in the author to express himself or herself问题5选项A.is understating the problemB.exaggerates one drawbackC.is too skeptical about the advantageD.overestimates the danger of losing text【答案】第1题:C第2题:D第3题:C第4题:A第5题:B【解析】1.细节事实题。

Lecture6-Marginalization

Lecture6-Marginalization

Lecture 6:Classification and marginalizationBayesian modeling1.Specify the generative model2.Specify the inference process: how the observer computes and reads out the posterior (on a single trial)3.Calculate behavior across many trials: distribution of the MAP estimateGenerative model•p (measurements | state of the world)•Describes the statistical structure of the world and/or task : how the measurements arise stochastically from the state-of-the-world variable.•Also called noise model or forward modelGenerative models seen so farsx sx Vx A Combining a measurement with a priorCue combinationC xordiscrete variable()()()()()()()1||11loglog 1||11p C x p x C p C d x p C x p x C p C ======−=−=−Log posterior ratio (or log odds ratio):()()()()|11loglog|11p x C p C p x C p C ===+=−=−log likelihood ratio (LLR)log prior ratioTuesday: binary decisions()0d x >MAP decision rule:dConfidence:Goals for today•See examples of other generative models in natural and psychophysical tasks•Understand how to derive the posterior for any generative model: marginalization •Work out a detailed example: visual search–Binary decision–Weighting by reliabilityExamples of other generative modelsClassificationTwo difficulties for an observer:•Noise: internal representation varies •Ambiguity: different causes, same soundAmbiguitya trapezoida rectangle on a roada weird wire frameCsxclassstimulusinternal representationGenerative modelAmbiguity p (s |C )Sensory noise p (x |s )Irrelevant variableKersten and Yuille, 2003The same object looks differently when viewed from a different angle.Generative modelIs θobject identity viewing angleimage Each node comes with a probabilitydistributionAB CBCAABCp(A)p(B|A)p (C|A)How does an observer do Bayesian inference for a given generative model?(Step 2)InferenceComputing the posterior distribution over the state-of-the-world variable based on the observationsAB CBCAABCGeneral approachpute the joint probability distribution by“following the arrows”.pute any conditional probabilitydistribution by marginalizing the jointdistribution. Conditional independence AB C()()()(),,||p A B C p A p B A p C A=()()()()()()()()(),,|| |,,||Ap A B C p A p B A p C A p A B Cp B C p A p B A p C A==∑DiscountingBCA()()()(),,|,p A B C p A p B p C A B =()()()()()()()()(),|,,||,BA Bp A p B p C A B p A C p A C p C p A p B p C A B ==∑∑Markov chainAB C()()()(),,||p A B C p A p B A p C B =()()()()()()()()()()(),,,,||,|,,||BBA BA Bp A B C p A p B A p C B p A C p A C p C p A B C p A p B A p C B ===∑∑∑∑In computing the posterior, a Bayesian observer marginalizes (sums or integrates) over every variable in the generative model other than the observations and the state-of-the-world variable of interest.More complex exampleBC DEF GAHomework: Compute p (A |E,F ) based on the conditional probabilities indicated in this generative model.Step 3: Response/estimate distribution()ˆargmax |ssp s x =MAP estimationEstimate distribution()ˆ|p ss ()()()()==>|ˆ1|Pr p x C p C C d x k Continuous:Binary:P r o b a b i l i t yˆs()ˆ|p ss False alarm rateD e t e c t i o n r a t e()0d x >Binary:Worked example: visual search(a bit more general than needed)Visual search Visual search in laboratoryWas the target present?Step 1: Generative model1. Choose trial type (target absent or present with equal probability)x x x x x x 3. At each location, set orientation based onwhether a target or a distractor is there: s T or s D2. If the target is present, choose its location (equal probabilities)4. Internal observations (Gaussian noise)XT s Ds target distractorGenerative modelTGlobal target presence:Yes/no T 1,…,T NLocal target presence:Yes/nos 1s 2s N…Stimuli…x Nx 2x 1InternalrepresentationsWrite down the probability distributionsassociated with each node()1212,0,0,0,,...,|0N N T T T p T T T T δδδ== ()112,0,1,011,,...,|1i N NN T T T i p T T T T Nδδδ===∑ ()()|0i i i D p s T s s δ==−()()|1i i i T p s T s s δ==−()()22221|2i i i x s i i ip x s eσπσ−−=()()010.5p T p T ====Step 2: InferenceCalculate the probability that the target is present (or absent) given the internal observations of the orientations:()121|,,...,N p T x x x =Do this using the generative model and the rules of probability calculusFollowing the arrowsTT 1,…,T Ns 1s 2s N ……x Nx 2x 1()()()()()()()()()1111111111,,...,,,...,,,...,,...,|,...,|,...,,...,|,...,,...,|||N N N N N N N N NN i i i i i p T T T s s x x p T p T T T p s s T T p x x s s p T p T T T p s T p x s ====∏Log posterior ratio()()111|,...,log0|,...,N N p T x x d p T x x ===()0d x >MAP decision rule:dConfidence:Evaluate the log posterior ratio()()()()()()()()()()()()111,...,11,...,1,0,1,0,...,11,01|,...,log0|,...,1,...,|1||log0,...,|0||1||logN N NNi i i i i T T i NNi i i i iT T i NNT T T i i i i iT T j i T p T x x d p T x x p T p T TT p s T p x s ds p T p T TT p s T p x s ds p s T p x s ds Nδδδδ=============∑∏∫∑∏∫∑∑∏∫ ()()()()()(),0,...,11||...|1|1log|0|NTiiiiiT T i Nj j j j j j jj j j jp s T p x s dsp s T p x s ds Np s T p x s ds δ======∑∏∫∫∑∫Final result•Bayesian integration rule for visual search•Log likelihood ratio of local target presence()()|1log|0i i i i i p x T d p x T ===11logNd i d eN==∑()()122i T D T D i x s s s s σ−+=−()()122i T D i T D i x s s d s s σ−+=−x x x x x x Local log likelihood ratio:weighting by reliability!Ts Ds targetdistractorDoes this look familiar?Yes! Discrimination (previous lecture)Step 3: Response/estimate distributionMAP estimationDistribution of MAP estimateProblem: cannot be calculated in closed form!What to do?()()()111,...,logx s s Ns s N i d x x eNσ−+−==>∑()()()()()()|11|01ˆ1|1Pr ,...,0ˆ1|0Pr ,...,0T N T Np T T d x x p T T d x x=====>===>x xMonte-Carlo simulation•Draw (e.g. in Matlab) many samples of x (trials) from generative model, both in T =0 and T =1 condition.•Compute LLR and MAP estimate on each trial.•Create “empirical distributions” of the LLR and of the MAP estimateExample LLR distribution-1001020Log likelihood ratio, dF r e q u e n c ytarget presenttarget absent N =6N =4N =8N =600.5100.51False alarm rateD e t e c t i o n r a t eROCLogic of visual search example1.Generative model2.Inference3.Estimate distribution usingMonte-Carlo simulationFalse alarm rateD e t e c t i o n r a t eLog likelihood ratio, d F r e q u e n c yTT ,…,T s s s ……x x x ()121|,,...,N p T x x x =1logd eN=∑()()x s s d s s σ−+=−s x V x A s A x A s Vx VCNumber of causesO n e c a u s eTwo causesCausal inferenceCompare visual searchs 1s 2x 2x 1CLs 1 , s 2x 2x 1CThe Bayesian modeling approach•Completely normative, no ad-hoc assumptions •A set recipe that always works •Very general, e.g. in visual search:–Multiple targets–Target more often in some locations than others –Distractors drawn from some distribution over orientation•Can be directly and rigorously compared with other models (Thursday)Caveats•Humans might not be using the priorsimposed by the experimenter (can to some extent be tested)•Observers must learn the structure of the generative model to perform Bayesian inference.•In real-life perception, generative models are unknown and very complex.Zhu et al., 2008。

lec2-13经典教材《金融时间序列分析》Ruey S. Tsay 英文第三版高清教材以及最新2013年完整版高清讲义

lec2-13经典教材《金融时间序列分析》Ruey S. Tsay 英文第三版高清教材以及最新2013年完整版高清讲义
2( −1) 2 )σ a .
This is called the mean-reversion of the AR(1) process. The variance of forecast error approaches Var[en( )] = 1 2 σ = Var(rt). a 1 − φ2 1
2 σa . 1−φ2 1
k 6. Autocorrelations: ρ1 = φ1, ρ2 = φ2 1 , etc. In general, ρk = φ1
and ACF ρk decays exponentially as k increases, 7. Forecast (minimum squared error): Suppose the forecast origin is n. For simplicity, we shall use the model representation in (1)
4
(g) Behavior of multi-step ahead forecasts. In general, for the -step ahead forecast at n, we have ˆ n ( ) = φ 1 xn , x the forecast error en( ) = an+ + φ1an+ −1 + · · · + φ1−1an+1, and the variance of forecast error Var[en( )] = (1 + φ2 1 + · · · + φ1 In particular, as → ∞, x ˆ n ( ) → 0, i.e., r ˆn( ) → µ.

1.1 On The Promise of Bayesian Inference for

1.1 On The Promise of Bayesian Inference for
tatistics in Astronomical Investigation
3
admitting uncertainty in terms of a prior distribution for (bjI). Also, expecting low counts implies b will be rather small (see also the paper by John Nousek, this volume, in discussion of low count radiation from SN1987A). Using a prior p(sjbI) that is uniform over a `large' range (and does not depend on b), Loredo proceeds to summary inferences based on the posterior p(sjnbI) in his equation (5.13). Throughout the paper, such uniform priors are adopted as a routine on the basis of representing suitable forms of `ignorance' about the quantity concerned. If any area of Bayesian inference has received too much attention during the last couple of decades it is surely the search for unique and `objective' representation of ignorance { see 4] for a recent and partial review of the eld. The maximum entropy school has been in uential in physical sciences, as referenced by Loredo, and particularly predominant in expounding the view that a single prior may be found, in any given situation, to represent vagueness in the sense of maximum entropy subject to certain `plausible' assumptions that typically stand for little more than mathematical convenience in determining a unique solution in the resulting MaxEnt framework. There is nothing unique, objective or otherwise scienti cally persuasive about uniform priors for location parameters, or any of the plethora of vague, reference or indi erence priors that abound. In investigations which admit an `objective' (de ned simply as consensus of informed observers) data model as here (ie. p(njsI)), analysis should necessarily involve study of sensitivity to qualitative and quantitative aspects of the prior, including assessments of pre-data predictive validity of the fdata modelg:fpriorg combination, and post-data determination of the mapping from prior to posterior for ranges of scienti cally plausible priors. The issue of pre-data validity is addressed through the implied (prior) R predictive distribution for the data, here p(njbI) = p(njsI)p(sjbI)ds. When n is observed, the value of this density function provides the normalising constant in Bayes' theorem (C ?1 in Loredo's equation (5.6)). Prior to the data, however, this distribution describes the investigator's view of experimental outcome. A uniform prior over a very large range translates essentially into a similar (though discrete) uniform p(njbI), which most observers would be quite concerned about as a plausible and scienti cally valid representation of expectations. The issue is particularly acute in problems of low counts and source detection when s (when non-zero) will be tend to be small | reasonable priors for s, and thus predictions about n, should surely re ect this. Competing `reference' priors (and there are many { 4]), lead to posteriors that can di er markedly with low counts s, though all such priors claim some form of `vagueness' and `uniformity' (on some scale). Scienti c investigation must involve careful and thorough consideration of initial information, modes of incorporation of such information in summary inferences, and exploration of sensitivity to prior assumptions (which includes model and data assumptions and well as priors for model parameters { and sometimes the distinction is unclear and even irrelevant

东南大学研究生一年级学术英语教科书答案chapter6-8

Unit Six1.3.11. We observed a stronger positive association for rectal than colon cancer.2. We found a positive association between red meat intake specifically and cancers of the esophagus and liver, and a borderline significant positive association for laryngeal cancer.3. Unexpectedly, we found an inverse association between red meat intake and endometrial cancer.1.3.21. Provide a brief synopsis of key findings, with particular emphasis on how the findings add to the body of pertinent knowledge.2. Summarize the result in relation to each research objective or hypothesis3. Relate findings back to the literature or the results reported by other researchers4. Discuss possible mechanisms and explanations for the findings. Compare study results with relevant findings from other published work. Briefly state literature search sources and methods. Use tables and figures to help summarize previous work when possible.5. Discuss the limitations of the present study and any methods used to minimize or compensate for those limitations, or mention any crucial future research directions.6. Conclude with a brief section that summarizes in a straightforward and circumspect manner the clinical implications of the work.2.12Like, like, Although, similarity, similar, most, most, But, equal2.2.12.3In our study, zinc supplementation did not result in a significant reduction in overall mortality in children aged 1–48 months in a population with high malaria transmission. However, there was a suggestion that the effect varied by age, with no effect on mortality in infants, and a marginally significant 18% reduction of mortality in children 12–48 months of age (p=0·045). This effect was mainly a consequence of fewer deaths from malaria and other infections. Any effect on mortality in this trial was in addition to a possible effect of vitamin A supplementation3.2Even though Arizona and Rhode Island are both states of the U.S., they are strikingly different in many ways. For example, the physical size of each state is different. Arizona is large, having an area of 114,000 square miles, whereas Rhode Island is only about a tenth the size, having an area of only 1,214 square miles. Another difference is in the size of the population of each state. Arizona has about four million people living in it, but Rhode Island has less than one million. The two states also differ in the kinds of natural environments that each has. For example, Arizona is a very dry state, consisting of large desert areas that do not receive much rainfall every year. However, Rhode Island is located in a temperate zone and receives an average of 44 inches of rain per year. In addition, while Arizona is a landlocked state and thus has no seashore, Rhode Island lies on the Atlantic Ocean and does have a significant coastline.3.3The following is taken from a discussion section of a research paper.DiscussionA thorough analysis of both …worst‟ and …best‟ rankings shows that the onsite containment technique leads to the best LCA result in the light of the taken hypotheses. Unlike other treatment techniques, onsite containment requires not only few materials (geosynthetics only) but alsosmall-scale excavation works. Actually the more a technique includes heavy technical operations involving materials and equipment, the worst is the result of LCA. This is the case for bio-leaching and offsite landfilling, which include, on the one hand, setting up the bio-leaching device, the treatment of leachates with lime, disposal of waste and cleaning of the site, and on the other hand, removal of soil and the transportation of huge quantities of materials over large distances.As mentioned above, besides the LCA, it is necessary to take into account the ability of techniques to substitute for each other as well as the environmental burdens which may be associated with them. Viewed in this light, it is worth noticing that bio-leaching and offsite landfilling provide complete remediation of the site, contrary to other treatment techniques. Bio-leaching consists of a real onsite decontamination of the polluted soil, which enables bequeathing of a clean site to coming generations. Nevertheless, in addition to a bad LCA result, this emergent technique is still poorly known and its efficiency is not quite proven for large-scale applications as yet. As regards offsite landfilling, if the site is left usable without any risk, the huge quantities of non-stabilized waste, which have to be disposed of in landfill, may disturb the organization of local waste management. This point emphasises the bad result of LCA.In return, if the favorable LCA result of onsite containment is due to light treatment operations, this very thing brings environmental issues up into the long-term. Indeed, only setting-up of awater-resistance device entails onsite storage of huge quantities of non-stabilized soil meaning that the initial problem is actually postponed, but not solved.As regards liming, which gives intermediate LCA results, an embankment of stabilized soil plays an important part in site rehabilitation. Indeed, in the absence of embankment, liming offers no chance of reuse for the whole site, whereas the site becomes partly reusable when an embankment of limed soil is achieved. However, stabilization provided by the liming technique is not reliable in the long term and it cannot be assured that the site will be safe for coming generations.To conclude, with the view to treating the site contaminated by sulfur in the short-term, the LCA has been a useful tool in determining the most environmentally friendly technique: onsite containment has been revealed to offer the best resource productivity.On the basis of these interesting results, it would be useful to take into account a wider range of environmental flows in order to get a more exhaustive inventory. And furthermore, a more conventional LCA format could be achieved by using impact categories (global warming,acidif ication…) as inputs in the multi-criteria analysis, instead of environmental flows.Life Cycle Assessment (LCA)Unit 72.1 Summerizing(Key: This text describes the experience of a Taiwanese man who has lived in Canada for several years. He considers Canadian women better off than Taiwanese. However, he notes some Canadian women feel nostalgic about the days when they received special courtesies. For example, formerly men opened doors for women or paid for their meals. At this time, most Canadians endeavor to treat men and women equally. Women today therefore are expected to cover the cost of their own meals. ) 2.2 Paraphrasing(Key for reference: Aries claims that beginning in the 1400s the way we viewed the family and the actual reality of the family changed. However, the change was so slow and subtle that people at that time did not see it. But the event itself, the growing importance of school, was quite striking. Prior to that time children were educated from the age of seven by being placed out or apprenticed to other families. Once schools were no longer limited to religious study, they replaced apprenticeship as society‟s means of educating the young and initiating them into society.)3.1 Key: EFABDC3.21) The present study is designed to determine what in San Francisco attracts visitors more,…2) The purpose of this investigation is to explore whether employees as well as managers have tobe equally trained for working in…3) This study set out to tackle the rate of juvenile delinquency in 1994 in U. S. A.4) The aim of this study is to determine whether education plays a role in….5) The project undertaken is to evaluate the marketing strategies currently applied by….6) The current study aims to determine whether children sent to daycare or preschool start….7) This project is aimed to explore how the discovery of … may change the way we treat….Unit 8 Writing Abstracts1.3.1 What does the abstract talk about1.3.2Decide how many elements this sample includes and how they function.2Language Focus2.1 Commonly used verbs in abstracts; tenses in abstracts2.2 More verbs and sentences patterns2.2 Verb tenses in abstractsRead the abstract above again and check the tenses in the abstract.3Writing Practice3.1 Abstract writing practice3.1.1A review of groundwater remediation in use today shows that new techniques are required that solve the problems of pump and treat, containment and in-situ treatment.3.1.2The use of a funnel and gate system via a trench has been examined in detail3.1.3The modeling involved an analysis of the effect of changing the lengths of the walls and gate, varying the permeability, and varying the number of gates.3.1.4An important factor in designing the walls is the residence time of the water in the gate or the contact time of the contaminant with the reactive media.3.1.5The results of the modeling and sensitivity analysis are presented such that they can be used as an aid to the design of permeable treatment walls.3.23.3Writing keywords4. Writing project4.1 Get prepared for writing an abstract 4.2 Outline an abstract4.3 According to the above table, draft an abstract and key words for the sample paper. Abstract:“Megacities” are defined as urban areas with more than ten million inhabi tants. By 2015 it is estimated that Asia (where much of the worldwide process of urbanization is taking place) may contain as many as 60 Megacities housing more than 600 million people in total. This number will dramatically increase over the next decades with more than 2 billion people living in Megacities by the end of this century. Low carbon performance is a fundamental aspect of the sustainable planning of a new urban development. Sustainable master planning has four components, namely operating energy use, embodied energy associated with buildings, energy supply infrastructures, another infrastructures such as transport, waste, water, sewage, etc. These aspects need to be understood to inform the concept design at its earliest stage, especially if designing to cater for the needs of global megacities where ramifications of poorly integrated planning could result in prof;ound andlong-lasting impacts on carbon and energy intensity. This paper describes how these aspects of low carbon planning and design can be assessed using urban scale modeling, namely the Energy and Environmental Prediction model (EEP-Urban),at a whole city and building plot level.Key words: Urban planning, High density, Urbanization, Energy modeling, Low carbon。

toc

Table of ContentsPreface .................................................................................................................................................................... x iii Organization ......................................................................................................................................................... x viii Program Committees ............................................................................................................................................ x ix Funding ................................................................................................................................................................. x xiii Invited Talks .......................................................................................................................................................... x xv Tutorial and Workshop Summaries ................................................................................................................. x xxiii The Role of Machine Learning in Business Optimization (1)Chid ApteFAB-MAP: Appearance-Based Place Recognition and Mapping using a Learned VisualVocabulary Model (3)Mark Cummins, Paul NewmanDiscriminative Latent Variable Models for Object Detection (11)Pedro Felzenszwalb, Ross Girshick, David McAllester, Deva RamananWeb-Scale Bayesian Click-Through rate Prediction for Sponsored Search Advertising inMicrosoft's Bing Search Engine (13)Thore Graepel, Joaquin Quiñonero Candela, Thomas Borchert, Ralf HerbrichMusic Plus One and Machine Learning (21)Christopher RaphaelClimbing the Tower of Babel: Unsupervised Multilingual Learning (29)Benjamin Snyder, Regina BarzilayDetecting Large-Scale System Problems by Mining Console Logs (37)Wei Xu, Ling Huang, Armando Fox, David Patterson, Michael I. JordanParticle Filtered MCMC-MLE with Connections to Contrastive Divergence (47)Arthur U. Asuncion, Qiang Liu, Alexander T. Ihler, Padhraic SmythSurrogating the surrogate: accelerating Gaussian-process-based global optimization with amixture cross-entropy algorithm (55)Remi Bardenet, Balazs KeglForgetting Counts : Constant Memory Inference for a Dependent Hierarchical Pitman-YorProcess (63)Nicholas Bartlett, David Pfau, Frank WoodRobust Formulations for Handling Uncertainty in Kernel Matrices (71)Sahely Bhadra, Sourangshu Bhattacharya, Chiranjib Bhattacharyya, Aharon Ben-talActive Learning for Networked Data (79)Mustafa Bilgic, Lilyana Mihalkova, Lise GetoorDistance dependent Chinese restaurant processes (87)David M. Blei, Peter FrazierCausal filter selection in microarray data (95)Gianluca Bontempi, Patrick E. MeyerLabel Ranking under Ambiguous Supervision for Learning Semantic Correspondences (103)Antoine Bordes, Nicolas Usunier, Jason WestonA Theoretical Analysis of Feature Pooling in Visual Recognition (111)Y-Lan Boureau, Jean Ponce, Yann LeCunMulti-agent Learning Experiments on Repeated Matrix Games (119)Bruno Bouzy, Marc MetivierLearning Tree Conditional Random Fields (127)Joseph K. Bradley, Carlos GuestrinFinding Planted Partitions in Nearly Linear Time using Arrested Spectral Clustering (135)Nader H. Bshouty, Philip M. LongFast boosting using adversarial bandits (143)Róbert Busa-Fekete, Balázs KéglModeling Transfer Learning in Human Categorization with the Hierarchical Dirichlet Process (151)Kevin R. Canini, Mikhail M. Shashkov, Thomas L. GriffithsTransfer Learning for Collective Link Prediction in Multiple Heterogenous Domains (159)Bin Cao, Nathan Nan Liu, Qiang YangThe Elastic Embedding Algorithm for Dimensionality Reduction (167)Miguel Á. Carreira-PerpiñánRandom Spanning Trees and the Prediction of Weighted Graphs (175)Nicolo Cesa-Bianchi, Claudio Gentile, Fabio Vitale, Giovanni ZappellaEfficient Learning with Partially Observed Attributes (183)Nicolo Cesa-Bianchi, Shai Shalev-Shwartz, Ohad ShamirConvergence, Targeted Optimality, and Safety in Multiagent Learning (191)Doran Chakraborty, Peter StoneStructured Output Learning with Indirect Supervision (199)Ming-Wei Chang, Vivek Srikumar, Dan Goldwasser, Dan RothDynamical Products of Experts for Modeling Financial Time Series (207)Yutian Chen, Max WellingLabel Ranking Methods based on the Plackett-Luce Model (215)Weiwei Cheng, Krzysztof Dembczynski, Eyke HüllermeierGraded Multilabel Classification: The Ordinal Case (223)Weiwei Cheng, Krzysztof Dembczynski, Eyke HüllermeierComparing Clusterings in Space (231)Michael H. Coen, M. Hidayath Ansari, Nathanael FillmoreTwo-Stage Learning Kernel Algorithms (239)Corinna Cortes, Mehryar Mohri, Afshin RostamizadehGeneralization Bounds for Learning Kernels (247)Corinna Cortes, Mehryar Mohri, Afshin RostamizadehFast Neighborhood Subgraph Pairwise Distance Kernel (255)Fabrizio Costa, Kurt De GraveMining Clustering Dimensions (263)Sajib Dasgupta, Vincent NgBottom-Up Learning of Markov Network Structure (271)Jesse Davis, Pedro DomingosBayes Optimal Multilabel Classification via Probabilistic Classifier Chains (279)Krzysztof Dembczynski, Weiwei Cheng, Eyke HüllermeierA Conditional Random Field for Multiple-Instance Learning (287)Thomas Deselaers, Vittorio FerrariAsymptotic Analysis of Generative Semi-Supervised Learning (295)Joshua V. Dillon, Krishnakumar Balasubramanian, Guy LebanonHeterogeneous Continuous Dynamic Bayesian Networks with Flexible Structure and Inter-TimeSegment Information Sharing (303)Frank Dondelinger, Sophie Lebre, Dirk HusmeierTemporal Difference Bayesian Model Averaging:A Bayesian Perspective on Adapting Lambda (311)Carlton Downey, Scott SannerHigh-Performance Semi-Supervised Learning using Discriminatively Constrained GenerativeModels (319)Gregory Druck, Andrew McCallumOn the Consistency of Ranking Algorithms (327)John C. Duchi, Lester W. Mackey, Michael I. JordanInverse Optimal Control with Linearly-Solvable MDPs (335)Krishnamurthy Dvijotham, Emanuel TodorovContinuous-Time Belief Propagation (343)Tal El-Hay, Ido Cohn, Nir Friedman, Raz KupfermanNonparametric Information Theoretic Clustering Algorithm (351)Lev Faivishevsky, Jacob GoldbergerFeature Selection as a One-Player Game (359)Romaric Gaudel, Michele SebagMultiscale Wavelets on Trees, Graphs and High Dimensional Data: Theory and Applications toSemi Supervised Learning (367)Matan Gavish, Boaz Nadler, Ronald R. CoifmanA Language-based Approach to Measuring Scholarly Impact (375)Sean M. Gerrish, David M. BleiBoosting Classifiers with Tightened L0-Relaxation Penalties (383)Noam Goldberg, Jonathan EcksteinBudgeted Nonparametric Learning from Data Streams (391)Ryan Gomes, Andreas KrauseLearning Fast Approximations of Sparse Coding (399)Karol Gregor, Yann LeCunBoosted Backpropagation Learning for Training Deep Modular Networks (407)Alexander Grubb, J. Andrew BagnellInteractive Submodular Set Cover (415)Andrew Guillory, Jeff BilmesLarge Scale Max-Margin Multi-Label Classification with Priors (423)Bharath Hariharan, Lihi Zelnik-Manor, S. V. N. Vishwanathan, Manik VarmaActive Learning for Multi-Task Adaptive Filtering (431)Abhay Harpale, Yiming YangBayesian Nonparametric Matrix Factorization for Recorded Music (439)Matthew D. Hoffman, David M. Blei, Perry R. CookMulti-Task Learning of Gaussian Graphical Models (447)Jean Honorio, Dimitris SamarasLearning Hierarchical Riffle Independent Groupings from Rankings (455)Jonathan Huang, Carlos GuestrinOn learning with kernels for unordered pairs (463)Martial Hue, Jean-Philippe VertA Simple Algorithm for Nuclear Norm Regularized Problems (471)Martin Jaggi, Marek SulovskýTelling cause from effect based on high-dimensional observations (479)Dominik Janzing, Patrik O. Hoyer, Bernhard SchölkopfProximal Methods for Sparse Hierarchical Dictionary Learning (487)Rodolphe Jenatton, Julien Mairal, Guillaume Obozinski, Francis Bach3D Convolutional Neural Networks for Human Action Recognition (495)Shuiwang Ji, Wei Xu, Ming Yang, Kai YuAccelerated dual decomposition for MAP inference (503)Vladimir Jojic, Stephen Gould, Daphne KollerEfficient Selection of Multiple Bandit Arms: Theory and Practice (511)Shivaram Kalyanakrishnan, Peter StoneA scalable trust-region algorithm with application to mixed-norm regression (519)Dongmin Kim, Suvrit Sra, Inderjit DhillonLocal Minima Embedding (527)Minyoung Kim, Fernando De la TorreGaussian Processes Multiple Instance Learning (535)Minyoung Kim, Fernando De la TorreTree-Guided Group Lasso for Multi-Task Regression with Structured Sparsity (543)Seyoung Kim, Eric P. XingLearning Markov Logic Networks Using Structural Motifs (551)Stanley Kok, Pedro DomingosOn Sparse Nonparametric Conditional Covariance Selection (559)Mladen Kolar, Ankur P. Parikh, Eric P. XingSubmodular Dictionary Selection for Sparse Representation (567)Andreas Krause, Volkan CevherImplicit Online Learning (575)Brian Kulis, Peter L. BartlettProbabilistic Backward and Forward Reasoning in Stochastic Relational Worlds (583)Tobias Lang, Marc ToussaintSupervised Aggregation of Classifiers using Artificial Prediction Markets (591)Nathan Lay, Adrian BarbuBayesian Multi-Task Reinforcement Learning (599)Alessandro Lazaric, Mohammad GhavamzadehAnalysis of a Classification-based Policy Iteration Algorithm (607)Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi MunosFinite-Sample Analysis of LSTD (615)Alessandro Lazaric, Mohammad Ghavamzadeh, Rémi MunosA fast natural Newton method (623)Nicolas Le Roux, Andrew FitzgibbonMaking Large-Scale Nystr•om Approximation Possible (631)Mu Li, James T. Kwok, Bao-Liang LuLearning Programs: A Hierarchical Bayesian Approach (639)Percy Liang, Michael I. Jordan, Dan KleinOn the Interaction between Norm and Dimensionality: Multiple Regimes in Learning (647)Percy Liang, Nati SrebroPower Iteration Clustering (655)Frank Lin, William W. CohenRobust Subspace Segmentation by Low-Rank Representation (663)Guangcan Liu, Zhouchen Lin, Yong YuRobust Graph Mode Seeking by Graph Shift (671)Hairong Liu, Shuicheng YanLarge Graph Construction for Scalable Semi-Supervised Learning (679)Wei Liu, Junfeng He, Shih-Fu ChangLearning Temporal Causal Graphs for Relational Time-Series Analysis (687)Yan Liu, Alexandru Niculescu-Mizil, Aurélie Lozano , Yong LuEfficient Reinforcement Learning with Multiple Reward Functions for Randomized ControlledTrial Analysis (695)Daniel J. Lizotte, Michael Bowling, Susan A. MurphyRestricted Boltzmann Machines are Hard to Approximately Evaluate or Simulate (703)Philip M. Long, Rocco A. ServedioMixed Membership Matrix Factorization (711)Lester Mackey, David Weiss, Michael I. JordanToward Off-Policy Learning Control with Function Approximation (719)Hamid Reza Maei, Csaba Szepesvari, Shalabh Bhatnagar, Richard S. SuttonConstructing States for Reinforcement Learning (727)M. M. Hassan MahmudDeep learning via Hessian-free optimization (735)James MartensLearning the Linear Dynamical System with ASOS (743)James MartensFrom Transformation-Based Dimensionality Reduction to Feature Selection (751)Mahdokht Masaeli, Glenn Fung, Jennifer G. DyRisk minimization, probability elicitation, and cost-sensitive SVMs (759)Hamed Masnadi-Shirazi, Nuno VasconcelosExploiting Data-Independence for Fast Belief-Propagation (767)Julian J. McAuley, Tibério S. CaetanoMetric Learning to Rank (775)Brian McFee, Gert LanckrietLearning Efficiently with Approximate Inference via Dual Losses (783)Ofer Meshi, David Sontag, Tommi Jaakkola, Amir GlobersonDeep Supervised t-Distributed Embedding (791)Renqiang Min, Laurens van der Maaten, Zineng Yuan, Anthony Bonner, Zhaolei ZhangNonparametric Return Distribution Approximation for Reinforcement Learning (799)Tetsuro Morimura, Masashi Sugiyama, Hisashi Kashima , Hirotaka Hachiya, Toshiyuki TanakaRectified Linear Units Improve Restricted Boltzmann Machines (807)Vinod Nair, Geoffrey E. HintonImplicit Regularization in Variational Bayesian Matrix Factorization (815)Shinichi Nakajima, Masashi SugiyamaEstimation of (near) low-rank matrices with noise and high-dimensional scaling (823)Sahand Negahban, Martin J. WainwrightMultiple Non-Redundant Spectral Clustering Views (831)Donglin Niu, Jennifer G. Dy, Michael I. JordanMultiagent Inductive Learning: an Argumentation-based Approach (839)Santiago Ontañón, Enric PlazaA Stick-Breaking Construction of the Beta Process (847)John Paisley, Aimee Zaas, Christopher W. Woods, Geoffrey S. Ginsburg, Lawrence CarinThe Margin Perceptron with Unlearning (855)Constantinos Panagiotakopoulos, Petroula TsampoukaBoosting for Regression Transfer (863)David Pardoe, Peter StoneFeature Selection Using Regularization in Approximate Linear Programs for Markov DecisionProcesses (871)Marek Petrik, Gavin Taylor, Ron Parr, Shlomo Zilberstein*Budgeted Distribution Learning of Belief Net Parameters (879)Liuyang Li, Barnabás Póczos, Csaba Szepesvári, Russ GreinerVariable Selection in Model-Based Clustering: To Do or To Facilitate (887)Leonard K. M. Poon, Nevin L. Zhang, Tao Chen, Yi WangApproximate Predictive Representations of Partially Observable Systems (895)Monica Dinculescu, Doina PrecupSpherical Topic Models (903)Joseph Reisinger, AustinWaters, Bryan Silverthorn, Raymond J. MooneySVM Classifier Estimation from Group Probabilities (911)Stefan RuepingClustering processes (919)Daniil RyabkoGaussian Process Change Point Models (927)Yunus Saatçi, Ryan Turner, Carl Edward RasmussenOnline Prediction with Privacy (935)Jun Sakuma, Hiromi AraiLearning Deep Boltzmann Machines using Adaptive MCMC (943)Ruslan SalakhutdinovActive Risk Estimation (951)Christoph Sawade , Niels Landwehr, Steffen Bickel, Tobias SchefferShould one compute the Temporal Difference fix point or minimize the Bellman Residual ? Theunified oblique projection view (959)Bruno ScherrerGaussian Covariance and Scalable Variational Inference (967)Matthias W. SeegerApplication of Machine Learning To Epileptic Seizure Detection (975)Ali Shoeb, John GuttagLearning optimally diverse rankings over large document collections (983)Aleksandrs Slivkins, Filip Radlinski, Sreenivas GollapudiHilbert Space Embeddings of Hidden Markov Models (991)Le Song, Sajid M. Siddiqi, Geoffrey Gordon, Alex SmolaCOFFIN : A Computational Framework for Linear SVMs (999)Soeren Sonnenburg, Vojtech FrancInternal Rewards Mitigate Agent Boundedness (1007)Jonathan Sorg, Satinder Singh, Richard LewisGaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design (1015)Niranjan Srinivas, Andreas Krause, Sham Kakade, Matthias SeegerUnsupervised Risk Strati cation in Clinical Datasets: Identifying Patients at Risk of RareOutcomes (1023)Zeeshan Syed, Ilan RubinfeldModel-based reinforcement learning with nearly tight exploration complexity bounds (1031)István Szita, Csaba SzepesváriTotal Variation and Cheeger Cuts (1039)Arthur Szlam, Xavier BressonLearning Sparse SVM for Feature Selection on Very High Dimensional Datasets (1047)Mingkui Tan, Li Wang, Ivor W. TsangDeep networks for robust visual recognition (1055)Yichuan Tang, Chris EliasmithA DC Programming Approach for Sparse Eigenvalue Problem (1063)Mamadou Thiao, Tao Pham Dinh, Hoai An Le ThiLeast-Squares Policy Iteration: Bias-Variance Trade-off in Control Problems (1071)Christophe Thiery, Bruno ScherrerAn Analysis of the Convergence of Graph Laplacians (1079)Daniel Ting, Ling Huang, Michael I. JordanA Fast Augmented Lagrangian Algorithm for Learning Low-Rank Matrices (1087)Ryota Tomioka, Taiji Suzuki, Masashi Sugiyama, Hisashi KashimaOne-sided Support Vector Regression for Multiclass Cost-sensitive Classification (1095)Han-Hsing Tu, Hsuan-Tien LinNon-Local Contrastive Objectives (1103)David Vickrey, Cliff Chiung-Yu Lin, Daphne KollerThe Translation-invariant Wishart-Dirichlet Process for Clustering Distance Data (1111)Julia E. Vogt, Sandhya Prabhakaran, Thomas J. Fuchs, Volker RothGeneralizing Apprenticeship Learning across Hypothesis Classes (1119)Thomas J. Walsh, Kaushik Subramanian, Michael L. Littman, Carlos DiukSequential Projection Learning for Hashing with Compact Codes (1127)Jun Wang, Sanjiv Kumar, Shih-Fu ChangA New Analysis of Co-Training (1135)Wei Wang, Zhi-Hua ZhouMulti-Class Pegasos on a Budget (1143)Zhuang Wang, Koby Crammer, Slobodan VuceticThe IBP Compound Dirichlet Process and its Application to Focused Topic Modeling (1151)Sinead Williamson, Chong Wang, Katherine A. Heller, David M. BleiOnline Streaming Feature Selection (1159)Xindong Wu, Kui Yu, Hao Wang, Wei DingClasses of Multiagent Q-learning Dynamics with -greedy Exploration (1167)Michael Wunder, Michael Littman, Monica BabesSimple and Efficient Multiple Kernel Learning by Group Lasso (1175)Zenglin Xu, Rong Jin, Haiqin Yang, Irwin King, Michael R. LyuSparse Gaussian Process Regression via l1 Penalization (1183)Feng Yan, Yuan (Alan) QiOnline Learning for Group Lasso (1191)Haiqin Yang, Zenglin Xu, Irwin King, Michael R. LyuLearning from Noisy Side Information by Generalized Maximum Entropy Model (1199)Tianbao Yang, Rong Jin, Anil K. JainConvergence of Least Squares Temporal Difference Methods Under General Conditions (1207)Huizhen YuImproved Local Coordinate Coding using Local Tangents (1215)Kai Yu, Tong ZhangProjection Penalties: Dimension Reduction without Loss (1223)Yi Zhang, Jeff SchneiderOTL: A Framework of Online Transfer Learning (1231)Peilin Zhao, Steven C.H. HoiConditional Topic Random Fields (1239)Jun Zhu, Eric P. XingCognitive Models of Test-Item Effects in Human Category Learning (1247)Xiaojin Zhu, Bryan R. Gibson, Kwang-Sung Jun, Timothy T. Rogers, Joseph Harrison, Chuck KalishModeling Interaction via the Principle of Maximum Causal Entropy (1255)Brian D. Ziebart, J. Andrew Bagnell, Anind K. Dey。

Independence

e a
Solve without the normalization constant for both B = true and B = false, and then compute the normalization constant and the final probabilities.
Example (Continued)
Example Query
P(Burglary | JohnCalls=true, MaryCalls=true) = <0.284,0.716> How can we compute such answers? One approach is to compute entire full joint distribution represented by the network, and use our earlier algorithm. But this would defeat the entire purpose of Bayes Nets.
Example (Continued)
Normalizing: (0.000592 + 0.001494) = 1.0 = 1.0/0.002086 = 479
P(B|j,m) = <0.284 , 0.716>
P(Y|X, Z) = P(Y|Z)
Example of the Kபைடு நூலகம்y Property
The following conditional independence holds: P(MaryCalls |JohnCalls, Alarm, Earthquake, Burglary) =
P(MaryCalls | Alarm)

精读4unit4 a drink in the passagelesson4_text appreciation【卧龙雪痕】


Protagonists of the story
Writing techniques of the story
Theme of the story
W
B
T
L
E
Lesson 4—A Drink in the Passage
I.
Text Analysis
invited to split a bottle with a white man in the passage of the latter’s apartment
Lesson 4—A Drink in the Passage
I.
Text Analysis
Structure
Part 1 (Paras. 1-6 ) about: Against what background and from whom the story comes Part 2 (Paras. 7-76) about: How the story goes
(4) Of course Majosi and Sola and the others
Questions: Who do you think Majosi and Sola were? Why did they strongly advise Simelane to and get
the prize personally?
their free communication and full understanding.
It is not just a wall imposed by apartheid laws, but a wall deeply rooted in their hearts.
  1. 1、下载文档前请自行甄别文档内容的完整性,平台不提供额外的编辑、内容补充、找答案等附加服务。
  2. 2、"仅部分预览"的文档,不可在线预览部分如存在完整性等问题,可反馈申请退款(可完整预览的文档不适用该条件!)。
  3. 3、如文档侵犯您的权益,请联系客服反馈,我们会尽快为您处理(人工客服工作时间:9:00-18:30)。

OnRetainingIntermediateProbabilisticModelsWhenBuildingBayesian
Networks

PrashantJ.DoshiandLloydG.GreenwaldDepartmentofMathematicsandComputerScienceDrexelUniversityJohnR.Clarke
DepartmentofSurgery
MCP-HahnemannUniversity

Introduction
TheprocessofbuildingaBayesiannetworkmayoccur
instages,inwhichintermediateBayesiannetworksare
builtduringpreliminaryprocessingandthenusedinthe
constructionoffurtherBayesiannetworks.Forexam-
ple,in(Doshi,Greenwald,&Clarke2001)wedescribe
awaytouseBayesiannetworkstomodelandcorrect
errorsinnoisydatasets.Thecorrecteddatasetsarethen
usedin(Doshi2001)tobuildpredictiveBayesiannet-
works.Throughthisprocesswebuiltnetworksthat
captureprobabilisticrelationshipsbetween412fields
ofdatafrom169,512patientsadmittedtotraumacen-
tersinPennsylvaniaandregisteredinthePennsylvania
TraumaSystemsFoundationTraumaRegistrybetween
1986and1999.
Intheprocessmentionedabove,intermediate
Bayesiannetworkswereusedtofindthemostlikelyval-
uesforfieldsfoundtohaveerrors.Thesemostlikely
valueswerethenusedinthecleanseddataset.How-
ever,inthesubsequentprocessofbuildingBayesian
networksfromthisdataset,wequestionedwhetheror
nottheseintermediatenetworksusedinerrorcorrec-
tionshouldhavebeenretained.Inotherwords,we
wantedtounderstandthetradeoffsinvolvedinretain-
ingthedistributionalinformationsummarizedineach
error-correctionnetworkratherthanjustretainingthe
mostlikelyvalueforeachcorrectedfield.Thisquestion
canbegeneralizedtoanyprocessofbuildingaBayesian
networkinstages.Thisnotedescribespreliminarywork
tounderstandtheseissues.
Animportantcomponentofthisstagednetwork
buildingprocessisthatcommonvariablesarerepre-
sentedfromonestagetothenext.Indatacleansing,
variablesusedtoqueryforerrordistributionsarethe
samevariablesthatareusedasevidencevariablesinthe
finalpredictivenetwork.Furthermore,thecontextvari-
ablesusedtomodelerrorsarealsorepresenteddirectly
inthefinalnetwork.Retainingdistributioninformation
canbeaccomplishedbyemployingnetworksfromearly
stageswithinthesubsequentnetworks.Commonvari-
ableslimitthepotentialblow-upinnetworksize.
Figure2:PatientoutcomepredictionBayesiannetwork
ofourtraumacaredatabase.
Thedatausedtotrainandtestthenetworksisob-
tainedfromastudyweconductedtoshowthatthe
timetolaparotomyforintra-abdominalbleedingfrom
traumadoesaffectsurvivalfordelaysupto90min-
utes(Clarkeetal.2001).Thedatarepresentspa-
tientswhowereadmittedtothetraumawardsin27
institutionsacrossthestateofPennsylvaniaforintra-
abdominalbleedingandwhoweremovedtotheoperat-
ingroomforlaparotomywithin90minutes.496such
patientswereidentifiedfromthedataandwelooked
at12variablesforeachpatient.Theisolateddatawas
randomlydividedintotrainingandtestsets,eachcon-
taininganequalnumberofrecords.Theconditional
probabilitytablesforthenetworkswerelearnedusing
thetrainingsetandinferencewascarriedoutusingthe
testsets.

TheerrorcorrectionmodelshowninFigure1em-
ploysacontext-drivenerrorcorrectionapproachde-
velopedin(Doshi,Greenwald,&Clarke2001).The
modeldisplaysaprobabilitydistributionovertheCor-
rect

1),systolic
bloodpressureonarrivalinthetraumaward,timeto
ED(EmergencyDepartment),timeinED,andrateof
bleedingtoPatient
Outcome)from
agivensetofevidencevalues.

PredictionusingtheMostLikelyValueThemost
likelyvalueforthevariableCorrect

Outcomeusingtestcasesthatcontainedthemost
likelyvaluesasfindingsforthenodepec

AliveDeadState
Errorrate=23.48%
PredictionusingtheProbabilityDistributionInor-
dertodirectlymakeuseoftheprobabilitydistributions
inferredfromtheerrormodelinthepatientprediction
model,wecombinedthetwonetworksbymergingthe
nodespecValueandcopyingallother
nodes.Findingsfromthetestinstanceswereentered
onlyfortheobservednodesi.e.nofindingswereen-
teredforthevariableCorrect
Outcome.Weobservedareduc-
tionof2.02%intheerrorratecomparedtotheerrorrate
intheprevioussectionasisevidentfromtheconfusion
matrixshownbelow.
PredictedActual

13224Alive
2962Dead

相关文档
最新文档