Bayesian Networks and Decision Graphs

Chapters

1Foundations of Probability and Graphical Models

2Bayesian Networks: Concepts and Construction

3Conditional Independence and d-Separation

4Exact Inference in Bayesian Networks

5Approximate Inference Techniques

Rejection sampling Likelihood weighting Importance sampling Gibbs sampling Metropolis-Hastings Rao-Blackwellization Loopy belief propagation Mean-field variational inference Expectation propagation Variational objectives Particle filtering Annealed importance sampling Convergence diagnostics Mixing and autocorrelation Bias-variance trade-offs

6Parameter Learning for Bayesian Networks

7Structure Learning and Causal Discovery

8Continuous and Hybrid Bayesian Networks

9Dynamic Bayesian Networks

10Decision Theory Foundations

11Decision Graphs and Influence Diagrams

12Algorithms for Decision Evaluation

13Causality and Interventions with Bayesian Networks

14Model Validation, Diagnostics, and Explainability

15Tools, Implementation, and Applications

Courses/Bayesian Networks and Decision Graphs /Approximate Inference Techniques

Approximate Inference Techniques

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Sampling, variational, and message passing methods for large or loopy networks.

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Mean-field variational inference

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