Lecture

Bayesian Networks: Factorization and Sampling

In course

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Description

This lecture covers the concept of Bayesian Networks, focusing on factorization and sampling methods. It explains how to sample from a distribution using Metropolis Hastings Algorithm and Variable Elimination, and how to factorize a joint distribution into conditional independencies using Directed Acyclic Graphs (DAGs). The instructor discusses the importance of active directed paths and conditional independence relationships in Bayesian Networks.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_xtdqmtcl

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Ontological neighbourhood

Mathematics

Discrete mathematics: Graph theory

Probability theory: Topics in probability theory

Statistics

Data collection: Statistical sampling

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