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

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

End-to-end modeling workflow Data preprocessing and encoding Feature discretization tools Efficient CPT storage Sparse and factorized representations Software libraries and frameworks Graph editors and visualization Probabilistic programming integration Acceleration and parallelism Reproducibility and versioning Deployment and serving Monitoring and model drift Domain applications: healthcare Domain applications: finance Capstone project guidelines

Courses/Bayesian Networks and Decision Graphs /Tools, Implementation, and Applications

Tools, Implementation, and Applications

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Practical workflows, software ecosystems, deployment, and real-world use cases.

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Data preprocessing and encoding

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