learning bayesian networks 1st edition by Richard Neapolitan ISBN 0130125342 978-0130125347

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ISBN 10:  0130125342
ISBN 13: 978-0130125347
Author: Richard Neapolitan

In this first edition book, methods are discussed for doing inference in Bayesian networks and inference diagrams. Hundreds of examples and problems allow readers to grasp the information. Some of the topics discussed include Pearl’s message passing algorithm, Parameter Learning: 2 Alternatives, Parameter Learning r Alternatives, Bayesian Structure Learning, and Constraint-Based Learning. For expert systems developers and decision theorists.

learning bayesian networks 1st Table of contents:

1. Introduction to Bayesian Networks

   • What is a Bayesian Network?
   • Applications of Bayesian Networks
   • Components of a Bayesian Network
   • Conditional Probability and Independence

2. Graph Theory and Probability Review

   • Graphical Models: An Overview
   • Directed Acyclic Graphs (DAGs)
   • Basic Probability Theory Review
   • Conditional Independence and Factorization

3. Modeling with Bayesian Networks

   • Representation of Knowledge in Bayesian Networks
   • Constructing a Bayesian Network
   • Inference in Bayesian Networks
   • Exact and Approximate Inference Algorithms

4. Learning Bayesian Networks from Data

   • Overview of Learning Algorithms
   • Supervised vs. Unsupervised Learning
   • Parameter Estimation
   • Structure Learning
   • Maximum Likelihood Estimation

5. Learning the Structure of Bayesian Networks

   • Score-based Methods
   • Search Algorithms for Structure Learning
   • Constraint-based Methods
   • Hybrid Methods

6. Inference in Bayesian Networks

   • Exact Inference: Variable Elimination, Junction Tree
   • Approximate Inference: Monte Carlo Methods
   • Markov Chain Monte Carlo (MCMC)
   • Belief Propagation

7. Dynamic Bayesian Networks

   • Introduction to Dynamic Bayesian Networks
   • Temporal Models and Hidden Markov Models (HMM)
   • Learning Dynamic Bayesian Networks
   • Inference in Dynamic Models

8. Applications of Bayesian Networks

   • Applications in Medical Diagnosis
   • Applications in Machine Learning
   • Decision Support Systems
   • Risk Analysis and Forecasting

9. Challenges and Future Directions

   • Scalability and Efficiency
   • Handling Missing Data
   • Bayesian Networks in Big Data
   • Ethical and Practical Considerations

Appendices

• A. Mathematical Foundations for Bayesian Networks
• B. Pseudocode for Inference Algorithms
• C. Further Reading and Resources
• D. Index

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