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Stanford University

Bayesian Networks 2 - Forward-Backward - Stanford CS221: AI

Stanford University via YouTube

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Overview

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This lecture explains probabilistic inference in Bayesian networks and hidden Markov models, covering forward-backward inference, particle filtering, and Gibbs sampling. It includes object-tracking and image-denoising demonstrations.

Syllabus

Introduction.
Review: Bayesian network.
Review: probabilistic inference.
Hidden Markov model inference.
Lattice representation.
Summary.
Hidden Markov models.
Review: beam search.
Step 1: propose.
weight.
Step 3: resample.
Application: object tracking.
Particle filtering demo.
Roadmap.
Gibbs sampling.

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Stanford Online

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