Mastering Probabilistic Graphical Models Using Python
Chapter 1: Bayesian Network Fundamentals
Chapter 2: Markov Network Fundamentals
Chapter 3: Inference – Asking Questions to Models
Chapter 4: Approximate Inference
Chapter 5: Model Learning – Parameter Estimation in Bayesian Networks
Chapter 6: Model Learning – Parameter Estimation in Markov Networks
Chapter 7: Specialized Models
Index
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