Bayesian Networks and Decision Graphs

Chapters

1Foundations of Probability and Graphical Models

Probability axioms Random variables and distributions Joint and marginal distributions Conditional probability and Bayes' rule Independence and conditional independence Chain rule of probability Factorization of joint distributions Graphical models overview Directed acyclic graphs (DAGs)I-maps and perfect maps d-separation intuition Markov blankets Plate notation Notation and conventions Common probability pitfalls

2Bayesian Networks: Concepts and Construction

3Conditional Independence and d-Separation

4Exact Inference in Bayesian Networks

5Approximate Inference Techniques

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 /Foundations of Probability and Graphical Models

Foundations of Probability and Graphical Models

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Core probability concepts and the rationale for representing distributions with graphs.

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Graphical models overview

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