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Modeling and Reasoning with Bayesian Networks 1st Edition by Adnan Darwiche ISBN 0521884381 9780521884389

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Author(s): Adnan Darwiche
ISBN(s): 9780521884389, 0521884381
Edition: 1
File Details: PDF, 10.11 MB
Year: 2009
Language: english
SKU: EB-1380316 Category: Tag:

Modeling and Reasoning with Bayesian Networks 1st Edition by Adnan Darwiche – Ebook PDF Instant Download/Delivery: 0521884381, 9780521884389
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ISBN 10: 0521884381
ISBN 13: 9780521884389
Author: Adnan Darwiche

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.

Modeling and Reasoning with Bayesian Networks 1st Table of contents:

1: Introduction
1.1: Automated Reasoning
1.2: Degrees of Belief
1.3: Probabilistic Reasoning
1.4: Bayesian Networks
1.5: What is not Covered in this Book

2: Propositional Logic
2.1: Introduction
2.2: Syntax of Propositional Sentences
2.3: Semantics of Propositional Sentences
2.4: The Monotonicity of Logical Reasoning
2.5: Multivalued Variables
2.6: Variable Instantiations and Related Notations
2.7: Logical Forms
Bibliographic Remarks
2.8: Exercises

3: Probability Calculus
3.1: Introduction
3.2: Degrees of Belief
3.3: Updating Beliefs
3.4: Independence
3.5: Further Properties of Beliefs
3.6: Soft Evidence
3.7: Continuous Variables as Soft Evidence
Bibliographic Remarks
3.8: Exercises

4: Bayesian Networks
4.1: Introduction
4.2: Capturing Independence Graphically
4.3: Parameterizing the Independence Structure
4.4: Properties of Probabilistic Independence
4.5: A Graphical Test of Independence
4.6: More on DAGs and Independence
Bibliographic Remarks
4.7: Exercises
4.8: Proofs

5: Building Bayesian Networks
5.1: Introduction
5.2: Reasoning with Bayesian Networks
5.3: Modeling with Bayesian Networks
5.4: Dealing with Large CPTs
5.5: The Significance of Network Parameters
Bibliographic Remarks
5.6: Exercises

6: Inference by Variable Elimination
6.1: Introduction
6.2: The Process of Elimination
6.3: Factors
6.4: Elimination as a Basis for Inference
6.5: Computing Prior Marginals
6.6: Choosing an Elimination Order
6.7: Computing Posterior Marginals
6.8: Network Structure and Complexity
6.9: Query Structure and Complexity
6.10: Bucket Elimination
Bibliographic Remarks
6.11: Exercises
6.12: Proofs

7: Inference by Factor Elimination
7.1: Introduction
7.2: Factor Elimination
7.3: Elimination Trees
7.4: Separators and Clusters
7.5: A Message-Passing Formulation
7.6: The Jointree Connection
7.7: The Jointree Algorithm: A Classical View
Bibliographic Remarks
7.8: Exercises
7.9: Proofs

8: Inference by Conditioning
8.1: Introduction
8.2: Cutset Conditioning
8.3: Recursive Conditioning
8.4: Any-Space Inference
8.5: Decomposition Graphs
8.6: The Cache Allocation Problem
Bibliographic Remarks
8.7: Exercises
8.8: Proofs

9: Models for Graph Decomposition
9.1: Introduction
9.2: Moral Graphs
9.3: Elimination Orders
9.4: Jointrees
9.5: Dtrees
9.6: Triangulated Graphs
Bibliographic Remarks
9.7: Exercises
9.8: Lemmas
9.9: Proofs

10: Most Likely Instantiations
10.1: Introduction
10.2: Computing MPE Instantiations
10.3: Computing MAP Instantiations
Bibliographic Remarks
10.4: Exercises
10.5: Proofs

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