Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 Propositional Logic
- 3 Probability Calculus
- 4 Bayesian Networks
- 5 Building Bayesian Networks
- 6 Inference by Variable Elimination
- 7 Inference by Factor Elimination
- 8 Inference by Conditioning
- 9 Models for Graph Decomposition
- 10 Most Likely Instantiations
- 11 The Complexity of Probabilistic Inference
- 12 Compiling Bayesian Networks
- 13 Inference with Local Structure
- 14 Approximate Inference by Belief Propagation
- 15 Approximate Inference by Stochastic Sampling
- 16 Sensitivity Analysis
- 17 Learning: The Maximum Likelihood Approach
- 18 Learning: The Bayesian Approach
- A Notation
- B Concepts from Information Theory
- C Fixed Point Iterative Methods
- D Constrained Optimization
- Bibliography
- Index
D - Constrained Optimization
Published online by Cambridge University Press: 23 February 2011
Book contents
- Frontmatter
- Contents
- Preface
- 1 Introduction
- 2 Propositional Logic
- 3 Probability Calculus
- 4 Bayesian Networks
- 5 Building Bayesian Networks
- 6 Inference by Variable Elimination
- 7 Inference by Factor Elimination
- 8 Inference by Conditioning
- 9 Models for Graph Decomposition
- 10 Most Likely Instantiations
- 11 The Complexity of Probabilistic Inference
- 12 Compiling Bayesian Networks
- 13 Inference with Local Structure
- 14 Approximate Inference by Belief Propagation
- 15 Approximate Inference by Stochastic Sampling
- 16 Sensitivity Analysis
- 17 Learning: The Maximum Likelihood Approach
- 18 Learning: The Bayesian Approach
- A Notation
- B Concepts from Information Theory
- C Fixed Point Iterative Methods
- D Constrained Optimization
- Bibliography
- Index
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- Modeling and Reasoning with Bayesian Networks , pp. 523 - 526Publisher: Cambridge University PressPrint publication year: 2009