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
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