CS262A Learning and Reasoning with Bayesian Network
Table of contents
CS262A Learning and Reasoning with Bayesian Network
- Preface
- 1 Introduction
- 2 Propositional Logic
- 3 Probability Calculus
- 4 Bayesian Networks
- 4 Bayesian Networks (continued)
- 5 Building Bayesian Networks
- 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
- 8 Inference by Conditioning
- 8 Inference by Conditioning (continued)
- 9 Models for Graph Decomposition
- 10 Most Likely Instantiations
- 11 The Complexity of Probabilistic Inference
- 12 Compiling Bayesian Networks
- 13 Inference with Local Structure
- 13 Inference with Local Structure (continued)
- 14 Approximate Inference by Belief Propagation
- 14.1 Introduction
- 14.2 The Belief Propagation Algorithm
- 14.3 Iterative Belief Propagation
- 14.4 The Semantics of IBP
- 14.5 Generalized Belief Propagation
- 14.6 Joingraphs
- 14.7 Iterative Joingraph Propagation
- 14.8 Edge-Deletion Semantics of Belief Propagation
- Bibliographic Remarks
- 14.9 Exercises
- 14.10 Proofs
- 15 Approximate Inference by Stochastic Sampling
- 16 Sensitivity Analysis
- 17 Learning: The Maximum Likelihood Approach
- 18 Learning: The Bayesian Approach
- Appendices
- Bibliography
- Index