MIT OpenCourseWare

Browse materialsMIT 6.438 · Fall 2014

Algorithms for Inference

Subject: Statistics

Art: Palmer, Edward, 1803-1872, Figures 113-123. Plate Electrical Machines, 1840 · Public domain

Instructor: Prof. Devavrat Shah
Term: Fall 2014
Level: Graduate
What's inside: 10 problem sets · 5 exams · 25 lecture notes · 17 other files · 5 web pages
Materials open: MIT OCW

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About this course

Source: MIT OpenCourseWare 6.438 Algorithms for Inference, Fall 2014, Prof. Devavrat Shah — original course page on ocw.mit.edu ↗. License: CC BY-NC-SA 4.0. Study order: MIT's own listing order, section by section. Taken from MIT's published course files, downloaded from ocw.mit.edu on September 23, 2026. How RedLectern is built.

Materials are grouped the way MIT organized them. A verified, course-wide order has not been established here.

Before you start

Prerequisites are not listed here. Check MIT's own syllabus for this course.

This course’s cited books, in our reading lists: Introduction to Probability · Linear Algebra and its Applications

How this course was organized

What's here, and what isn't

MIT's own materials here did not support a verified, course-wide order, so they are grouped by section instead.

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MIT's page format for this item wasn't recognized clearly enough to place it safely. (22 items)
Referenced in a table, but without MIT's own verified order, so it isn't used to claim a sequence. (2 items)

Materials by section

Grouped by section, as MIT organized them. Not sequenced or ordered.

Lecture Notes

24 files

Recitations

13 files

Assignments

14 files

Exams

MIT OpenCourseWare, Prof. Devavrat Shah, 6.438 Algorithms for Inference, Fall 2014.

License: CC BY-NC-SA 4.0 · Edition: Fall 2014 · Open the original course on OCW ↗

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Algorithms for Inference

Before you start

What's here, and what isn't

Not included here

Materials by section

Lecture Notes

Recitations

Assignments

Exams

Lecture 1: Course Overview (PDF) Preliminaries (PDF)

Lecture 2: Directed Graphical Models (PDF)

Lecture 3: Undirected Graphical Models (PDF)

Lecture 4: Factor Graphs and Comparing Graphical Model Types (PDF)

Lecture 5: Minimal I-Maps, Chordal Graphs, Trees, and Markov Chains (PDF)

Lecture 6: Gaussian Graphical Models (PDF)

Lecture 7: Inference On Graphs: The Elimination Algorithm (PDF)

Lecture 8: Inference On Trees: Sum-Product Algorithm (PDF)

Lecture 9: Forward-Backward Algorithm, Sum-Product On Factor Graphs (PDF)

Lecture 10: Sum-Product On Factor Graphs, MAP Elimination (PDF)

Lecture 11: The Max-Product Algorithm (PDF)

Lecture 12: Gaussian Belief Propagation (PDF)

Lecture 13: BP on Gaussian Hidden Markov Models: Kalman Filtering (PDF)

Lecture 14: The Junction Tree Algorithm (PDF)

Lecture 17: Variational Inference (PDF)

Lecture 18: Markov Chain Monte Carlo Methods and Approximate MAP (PDF)

Lecture 19: Approximate Inference: Importance Sampling and Particle Filters (PDF)

Lecture 20: Learning Graphical Models (PDF)

Lecture 21: Learning Parameters of an Undirected Graphical Model (PDF)

Lecture 22: Parameter Estimation from Partial Observations (PDF)

Lecture 23: Learning Structure in Directed Graphs (PDF)

Lecture 24: Learning Exponential Family Models (PDF)