Kiran R Karkera - Building Probabilistic Graphical Models with Python [2014, PDF, ENG]

1: Probability
The theory of probability
Goals of probabilistic inference
Conditional probability
The chain rule
The Bayes rule
Interpretations of probability
Random variables
Marginal distribution
Joint distribution
Independence
Conditional independence
Types of queries
Summary
2: Directed Graphical Models
Graph terminology
Independence and independent parameters
The Bayes network
Reasoning patterns
D-separation
Factorization and I-maps
The Naive Bayes model
Summary
3: Undirected Graphical Models
Pairwise Markov networks
The Gibbs distribution
An induced Markov network
Factorization
Flow of influence
Active trail and separation
Structured prediction
The factorization-independence tango
Summary
4: Structure Learning
The structure learning landscape
Constraint-based structure learning
Score-based learning
Summary
5: Parameter Learning
The likelihood function
Parameter learning example using MLE
MLE for Bayesian networks
Bayesian parameter learning example using MLE
Data fragmentation
Effects of data fragmentation on parameter estimation
Bayesian parameter estimation
Bayesian estimation for the Bayesian network
Example of Bayesian estimation
Summary
6: Exact Inference Using Graphical Models
Complexity of inference
Using the Variable Elimination algorithm
The tree algorithm
Summary
7: Approximate Inference Methods
The optimization perspective
Steps in the LBP algorithm
Sampling-based methods
Summary