標題: Titlebook: Bayesian Networks and Decision Graphs; Finn V. Jensen,Thomas D. Nielsen Textbook 2007Latest edition Springer-Verlag New York 2007 Analysis [打印本頁] 作者: 投射技術 時間: 2025-3-21 17:06
書目名稱Bayesian Networks and Decision Graphs影響因子(影響力)
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書目名稱Bayesian Networks and Decision Graphs網絡公開度學科排名
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書目名稱Bayesian Networks and Decision Graphs讀者反饋
書目名稱Bayesian Networks and Decision Graphs讀者反饋學科排名
作者: 松雞 時間: 2025-3-21 21:28
Michael St.Pierre,Gesine HofingerThe primary issue in dealing with a decision problem is to determine an optimal strategy, but other issues may be relevant. This chapter deals with value of information, the relevant past and future for a decision, and the sensitivity of decisions with respect to parameters.作者: 花爭吵 時間: 2025-3-22 01:37
Bayesian Networks as ClassifiersYou receive a mail and wish to determine whether it is spam; you see a bird and wish to determine its species; you examine a patient and wish to diagnose him. These are only a few examples of the very common human task, classification.作者: padding 時間: 2025-3-22 05:56
Methods for Analyzing Decision ProblemsThe primary issue in dealing with a decision problem is to determine an optimal strategy, but other issues may be relevant. This chapter deals with value of information, the relevant past and future for a decision, and the sensitivity of decisions with respect to parameters.作者: 來自于 時間: 2025-3-22 09:12
Prerequisites on Probability Theoryty theory before, and the purpose of this section is simply to brush up on some of the basic concepts and to introduce some of the notation used in the later chapters. Sections 1.1–1.3 are prerequisites for Section 2.3 and forward. Section 1.4 is a prerequisite for Chapter 4. and Section 1.5 is a prerequisite for Chapter 6 and Chapter 7.作者: 極大的痛苦 時間: 2025-3-22 15:15 作者: aerobic 時間: 2025-3-22 20:50 作者: MAIZE 時間: 2025-3-22 23:39 作者: Axillary 時間: 2025-3-23 03:16 作者: fructose 時間: 2025-3-23 08:09 作者: 獨輪車 時間: 2025-3-23 10:03
Personalauswahl und Potenzialanalysety theory before, and the purpose of this section is simply to brush up on some of the basic concepts and to introduce some of the notation used in the later chapters. Sections 1.1–1.3 are prerequisites for Section 2.3 and forward. Section 1.4 is a prerequisite for Chapter 4. and Section 1.5 is a prerequisite for Chapter 6 and Chapter 7.作者: Charitable 時間: 2025-3-23 14:32 作者: Occlusion 時間: 2025-3-23 20:35
Michael St.Pierre,Gesine Hofingerding these computer models is to use them when taking decisions. In other words, the probabilities provided by the network are used to support some kind of decision making. In principle, there are two kinds of decisions, namely . and ..作者: CALL 時間: 2025-3-24 00:12
https://doi.org/10.1007/978-0-387-68282-2Analysis; Bayesian network; Markov decision process; algorithms; artificial intelligence; computer; learni作者: Scintillations 時間: 2025-3-24 03:18 作者: 600 時間: 2025-3-24 07:23 作者: 莊嚴 時間: 2025-3-24 11:14
https://doi.org/10.1007/978-3-662-59759-0about relevance in causal networks; is knowledge of A relevant for my belief about .? These sections deal with reasoning under uncertainty in general. Next, Bayesian networks are defined as causal networks with the strength of the causal links represented as conditional probabilities. Finally, the c作者: Intersect 時間: 2025-3-24 16:45
Human Factors of Stereoscopic 3D Displays the calculations in Section 2.6, it is a tedious job to perform evidence transmission even for very simple Bayesian networks. Fortunately, software tools that can do the calculation job for us are available. In the rest of this book, we assume that the reader has access to such a system (some URLs 作者: PUT 時間: 2025-3-24 19:49 作者: 珍奇 時間: 2025-3-25 00:43 作者: 寬大 時間: 2025-3-25 03:53 作者: florid 時間: 2025-3-25 11:26
Human Factors of Stereoscopic 3D Displays, and you are asked to reconstruct the Bayesian network from the cases. This is the general setting for structural learning of Bayesian networks. In the real world you cannot be sure that the cases are actually sampled from a “true” network, but this we will assume. We will also assume that the samp作者: KEGEL 時間: 2025-3-25 14:13 作者: Offset 時間: 2025-3-25 17:05
Michael St.Pierre,Gesine Hofinger complexity of the problem to the computer. For problems with a finite time horizon, the computer may fold out the specification to a decision tree and determine an optimal strategy by averaging out and folding back as described in Section 9.3.3. However, the calculations may be intractable, and in 作者: 憤怒事實 時間: 2025-3-25 23:52 作者: 輕彈 時間: 2025-3-26 00:17 作者: 羞辱 時間: 2025-3-26 05:25 作者: 沒有希望 時間: 2025-3-26 10:28
Causal and Bayesian Networksabout relevance in causal networks; is knowledge of A relevant for my belief about .? These sections deal with reasoning under uncertainty in general. Next, Bayesian networks are defined as causal networks with the strength of the causal links represented as conditional probabilities. Finally, the c作者: 租約 時間: 2025-3-26 13:40 作者: tenuous 時間: 2025-3-26 20:51 作者: 桶去微染 時間: 2025-3-26 22:32 作者: 責問 時間: 2025-3-27 04:12
Parameter estimationes. On the other hand, you have access to a database of cases, i.e., a set of simultaneous values for some of the variables in .. You can now use these cases to estimate the parameters of the model, namely the conditional probabilities. In this chapter we consider two approaches for handling this pr作者: Plaque 時間: 2025-3-27 08:37 作者: Lignans 時間: 2025-3-27 09:39 作者: Exaggerate 時間: 2025-3-27 14:13 作者: Presbycusis 時間: 2025-3-27 19:39 作者: 存心 時間: 2025-3-28 01:21 作者: Indigence 時間: 2025-3-28 05:53 作者: phase-2-enzyme 時間: 2025-3-28 06:49
Building Modelsools that can do the calculation job for us are available. In the rest of this book, we assume that the reader has access to such a system (some URLs are given in the Preface). Therefore, we can start by concentrating on how to use Bayesian networks in model building and defer a presentation of methods for probability updating to Chapter 4.作者: dagger 時間: 2025-3-28 13:27
Belief Updating in Bayesian Networks with the number of variables, we look for more efficient methods. Unfortunately, no method guarantees a tractable calculation task. However, the method presented here represents a substantial improvement, and it is among the most efficient methods known.作者: Rejuvenate 時間: 2025-3-28 16:31 作者: 閑聊 時間: 2025-3-28 22:04
Solution Methods for Decision Graphsoting, which has next to no temporal ordering, and for which the decision trees tend to be intractably large. In Section 10.6 we present two methods for solving MDPs, and a method for solving POMDPs is indicated. The last section presents LIMIDs, which is a way of approximating influence diagrams by limiting the memory of the decision maker.作者: 起草 時間: 2025-3-29 00:31
https://doi.org/10.1007/978-3-662-59759-0hain rule for Bayesian networks is presented. The chain rule is the property that makes Bayesian networks a very powerful tool for representing domains with inherent uncertainty. The sections on Bayesian networks assume knowledge of probability calculus as laid out in Sections 1.1–1.4.作者: accomplishment 時間: 2025-3-29 03:30 作者: 乳白光 時間: 2025-3-29 08:50 作者: bleach 時間: 2025-3-29 14:56 作者: 炸壞 時間: 2025-3-29 18:32 作者: machination 時間: 2025-3-29 21:57
Human Factors of Stereoscopic 3D Displays.. Furthermore, we assume that all links in . are ., i.e., if you remove a link, then the resulting network cannot represent .. Mathematically, it can be expressed as follows: if pa(.) are the parents of ., and . is any of them, then there are two states . and . of . and a configuration . of the other parents such that ..作者: gorgeous 時間: 2025-3-29 23:57
Learning the Structure of Bayesian Networks.. Furthermore, we assume that all links in . are ., i.e., if you remove a link, then the resulting network cannot represent .. Mathematically, it can be expressed as follows: if pa(.) are the parents of ., and . is any of them, then there are two states . and . of . and a configuration . of the other parents such that ..作者: 流浪 時間: 2025-3-30 06:26
Federico Pio GentileMoves towards a better appreciation of the often-invisible implications of “commodified” productions.Focuses on the Canadian scenario, offering a counterpoint to existing work on US crime series.Makes
