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Modeling and Reasoning with Bayesian Networks [Mīkstie vāki]

4.28/5 (32 ratings by Goodreads)
(University of California, Los Angeles)
  • Formāts: Paperback / softback, 562 pages, height x width x depth: 254x178x29 mm, weight: 960 g, Worked examples or Exercises; 64 Tables, unspecified; 10 Halftones, unspecified; 236 Line drawings, unspecified
  • Izdošanas datums: 07-Aug-2014
  • Izdevniecība: Cambridge University Press
  • ISBN-10: 1107678420
  • ISBN-13: 9781107678422
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  • Mīkstie vāki
  • Cena: 83,33 €
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Modeling and Reasoning with Bayesian NetworksPalielināt
  • Formāts: Paperback / softback, 562 pages, height x width x depth: 254x178x29 mm, weight: 960 g, Worked examples or Exercises; 64 Tables, unspecified; 10 Halftones, unspecified; 236 Line drawings, unspecified
  • Izdošanas datums: 07-Aug-2014
  • Izdevniecība: Cambridge University Press
  • ISBN-10: 1107678420
  • ISBN-13: 9781107678422
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9781107678422.html
Keywords:
This book is a thorough introduction to the formal foundations and practical applications of Bayesian networks. It provides an extensive discussion of techniques for building Bayesian networks that model real-world situations, including techniques for synthesizing models from design, learning models from data, and debugging models using sensitivity analysis. It also treats exact and approximate inference algorithms at both theoretical and practical levels. The treatment of exact algorithms covers the main inference paradigms based on elimination and conditioning and includes advanced methods for compiling Bayesian networks, time-space tradeoffs, and exploiting local structure of massively connected networks. The treatment of approximate algorithms covers the main inference paradigms based on sampling and optimization and includes influential algorithms such as importance sampling, MCMC, and belief propagation. The author assumes very little background on the covered subjects, supplying in-depth discussions for theoretically inclined readers and enough practical details to provide an algorithmic cookbook for the system developer.

Recenzijas

' both practical and advanced The first five chapters are sufficient for students and practitioners to gain the necessary knowledge in order to build Bayesian networks for moderately sized applications with the aid of a software tool All major inference methods are covered in later chapters which allow researchers and software developers to implement their own software systems tailored to their needs It is a comprehensive book that can be used for self study by students and newcomers to the field or as a companion for courses on probabilistic reasoning. Experienced researchers may also find deeper information on some topics. In my opinion, the book should definitely be [ on] the bookshelf of everyone who teaches Bayesian networks and builds probabilistic reasoning agents.' Artificial Intelligence '[ This] book will make an excellent textbook; it covers topics suitable for both undergraduate and graduate courses. It will also help practitioners get a firm grasp of the fundamentals of modeling and inference with BNs, as well as some recent advances.' ACM Computing Reviews

Papildus informācija

This book introduces the formal foundations and practical applications of Bayesian networks.
Preface xi
1 Introduction
1(12)
1.1 Automated Reasoning
1(3)
1.2 Degrees of Belief
4(2)
1.3 Probabilistic Reasoning
6(2)
1.4 Bayesian Networks
8(4)
1.5 What Is Not Covered in This Book
12(1)
2 Prepositional Logic
13(14)
2.1 Introduction
13(1)
2.2 Syntax of Propositional Sentences
13(2)
2.3 Semantics of Propositional Sentences
15(3)
2.4 The Monotonicity of Logical Reasoning
18(1)
2.5 Multivalued Variables
19(1)
2.6 Variable Instantiations and Related Notations
20(1)
2.7 Logical Forms
21(4)
Bibliographic Remarks
24(1)
2.8 Exercises
25(2)
3 Probability Calculus
27(26)
3.1 Introduction
27(1)
3.2 Degrees of Belief
27(3)
3.3 Updating Beliefs
30(4)
3.4 Independence
34(3)
3.5 Further Properties of Beliefs
37(2)
3.6 Soft Evidence
39(7)
3.7 Continuous Variables as Soft Evidence
46(3)
Bibliographic Remarks
48(1)
3.8 Exercises
49(4)
4 Bayesian Networks
53(23)
4.1 Introduction
53(1)
4.2 Capturing Independence Graphically
53(3)
4.3 Parameterizing the Independence Structure
56(2)
4.4 Properties of Probabilistic Independence
58(5)
4.5 A Graphical Test of Independence
63(5)
4.6 More on DAGs and Independence
68(4)
Bibliographic Remarks
71(1)
4.7 Exercises
72(3)
4.8 Proofs
75(1)
5 Building Bayesian Networks
76(50)
5.1 Introduction
76(1)
5.2 Reasoning with Bayesian Networks
76(8)
5.3 Modeling with Bayesian Networks
84(30)
5.4 Dealing with Large CPTs
114(5)
5.5 The Significance of Network Parameters
119(3)
Bibliographic Remarks
121(1)
5.6 Exercises
122(4)
6 Inference by Variable Elimination
126(26)
6.1 Introduction
126(1)
6.2 The Process of Elimination
126(2)
6.3 Factors
128(3)
6.4 Elimination as a Basis for Inference
131(2)
6.5 Computing Prior Marginals
133(2)
6.6 Choosing an Elimination Order
135(3)
6.7 Computing Posterior Marginals
138(3)
6.8 Network Structure and Complexity
141(2)
6.9 Query Structure and Complexity
143(4)
6.10 Bucket Elimination
147(1)
Bibliographic Remarks
148(1)
6.11 Exercises
148(3)
6.12 Proofs
151(1)
7 Inference by Factor Elimination
152(26)
7.1 Introduction
152(1)
7.2 Factor Elimination
153(2)
7.3 Elimination Trees
155(2)
7.4 Separators and Clusters
157(2)
7.5 A Message-Passing Formulation
159(5)
7.6 The Jointree Connection
164(2)
7.7 The Jointree Algorithm: A Classical View
166(7)
Bibliographic Remarks
172(1)
7.8 Exercises
173(3)
7.9 Proofs
176(2)
8 Inference by Conditioning
178(24)
8.1 Introduction
178(1)
8.2 Cutset Conditioning
178(3)
8.3 Recursive Conditioning
181(7)
8.4 Any-Space Inference
188(1)
8.5 Decomposition Graphs
189(3)
8.6 The Cache Allocation Problem
192(5)
Bibliographic Remarks
196(1)
8.7 Exercises
197(1)
8.8 Proofs
198(4)
9 Models for Graph Decomposition
202(41)
9.1 Introduction
202(1)
9.2 Moral Graphs
202(1)
9.3 Elimination Orders
203(13)
9.4 Join trees
216(8)
9.5 Dtrees
224(5)
9.6 Triangulated Graphs
229(3)
Bibliographic Remarks
231(1)
9.7 Exercises
232(2)
9.8 Lemmas
234(2)
9.9 Proofs
236(7)
10 Most Likely Instantiations
243(27)
10.1 Introduction
243(1)
10.2 Computing MPE Instantiations
244(14)
10.3 Computing MAP Instantiations
258(7)
Bibliographic Remarks
264(1)
10.4 Exercises
265(2)
10.5 Proofs
267(3)
11 The Complexity of Probabilistic Inference
270(17)
11.1 Introduction
270(1)
11.2 Complexity Classes
271(1)
11.3 Showing Hardness
272(2)
11.4 Showing Membership
274(1)
11.5 Complexity of MAP on Polytrees
275(1)
11.6 Reducing Probability of Evidence to Weighted Model Counting
276(4)
11.7 Reducing MPE to W-MAXSAT
280(3)
Bibliographic Remarks
283(1)
11.8 Exercises
283(1)
11.9 Proofs
284(3)
12 Compiling Bayesian Networks
287(26)
12.1 Introduction
287(2)
12.2 Circuit Semantics
289(2)
12.3 Circuit Propagation
291(9)
12.4 Circuit Compilation
300(6)
Bibliographic Remarks
306(1)
12.5 Exercises
306(3)
12.6 Proofs
309(4)
13 Inference with Local Structure
313(27)
13.1 Introduction
313(1)
13.2 The Impact of Local Structure on Inference Complexity
313(6)
13.3 CNF Encodings with Local Structure
319(4)
13.4 Conditioning with Local Structure
323(3)
13.5 Elimination with Local Structure
326(11)
Bibliographic Remarks
336(1)
13.6 Exercises
337(3)
14 Approximate Inference by Belief Propagation
340(38)
14.1 Introduction
340(1)
14.2 The Belief Propagation Algorithm
340(3)
14.3 Iterative Belief Propagation
343(3)
14.4 The Semantics of IBP
346(3)
14.5 Generalized Belief Propagation
349(1)
14.6 Joingraphs
350(2)
14.7 Iterative Joingraph Propagation
352(2)
14.8 Edge-Deletion Semantics of Belief Propagation
354(11)
Bibliographic Remarks
364(1)
14.9 Exercises
365(5)
14.10 Proofs
370(8)
15 Approximate Inference by Stochastic Sampling
378(39)
15.1 Introduction
378(1)
15.2 Simulating a Bayesian Network
378(3)
15.3 Expectations
381(4)
15.4 Direct Sampling
385(7)
15.5 Estimating a Conditional Probability
392(1)
15.6 Importance Sampling
393(8)
15.7 Markov Chain Simulation
401(7)
Bibliographic Remarks
407(1)
15.8 Exercises
408(3)
15.9 Proofs
411(6)
16 Sensitivity Analysis
417(22)
16.1 Introduction
417(1)
16.2 Query Robustness
417(10)
16.3 Query Control
427(7)
Bibliographic Remarks
433(1)
16.4 Exercises
434(1)
16.5 Proofs
435(4)
17 Learning: The Maximum Likelihood Approach
439(38)
17.1 Introduction
439(2)
17.2 Estimating Parameters from Complete Data
441(3)
17.3 Estimating Parameters from Incomplete Data
444(11)
17.4 Learning Network Structure
455(6)
17.5 Searching for Network Structure
461(6)
Bibliographic Remarks
466(1)
17.6 Exercises
467(3)
17.7 Proofs
470(7)
18 Learning: The Bayesian Approach
477(38)
18.1 Introduction
477(2)
18.2 Meta-Networks
479(3)
18.3 Learning with Discrete Parameter Sets
482(7)
18.4 Learning with Continuous Parameter Sets
489(9)
18.5 Learning Network Structure
498(7)
Bibliographic Remarks
504(1)
18.6 Exercises
505(3)
18.7 Proofs
508(7)
A Notation 515(2)
B Concepts from Information Theory 517(3)
C Fixed Point Iterative Methods 520(3)
D Constrained Optimization 523(4)
Bibliography 527(14)
Index 541
Adnan Darwiche is a Professor in the Department of Computer Science at the University of California, Los Angeles.