Algorithms for Inference

Lecture Notes

LEC #	LECTURE TOPICS AND NOTES
1	Course Overview (PDF) Preliminaries (PDF)
2	Directed Graphical Models (PDF)
3	Undirected Graphical Models (PDF)
4	Factor Graphs and Comparing Graphical Model Types (PDF)
5	Minimal I-Maps, Chordal Graphs, Trees, and Markov Chains (PDF)
6	Gaussian Graphical Models (PDF)
7	Inference On Graphs: The Elimination Algorithm (PDF)
8	Inference On Trees: Sum-Product Algorithm (PDF)
9	Forward-Backward Algorithm, Sum-Product On Factor Graphs (PDF)
10	Sum-Product On Factor Graphs, MAP Elimination (PDF)
11	The Max-Product Algorithm (PDF)
12	Gaussian Belief Propagation (PDF)
13	BP on Gaussian Hidden Markov Models: Kalman Filtering (PDF)
14	The Junction Tree Algorithm (PDF)
15–16	Loopy Belief Propagation and its Properties (PDF)
17	Variational Inference (PDF)
18	Markov Chain Monte Carlo Methods and Approximate MAP (PDF)
19	Approximate Inference: Importance Sampling and Particle Filters (PDF)
20	Learning Graphical Models (PDF)
21	Learning Parameters of an Undirected Graphical Model (PDF)
22	Parameter Estimation from Partial Observations (PDF)
23	Learning Structure in Directed Graphs (PDF)
24	Learning Exponential Family Models (PDF)