Markov Chains: Applications and Sampling Methods

In course

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Description

This lecture covers the basics of Markov chains, including the main concepts like state distribution, ergodic chains, and importance sampling. It also delves into algorithmic applications and sampling methods such as Monte Carlo and rejection sampling.

Instructors (2)

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Official source

https://mediaspace.epfl.ch/media/0_6psouyc0

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Ontological neighbourhood

Statistics

Statistical inference: Bayesian inference

Data collection: Statistical sampling

Mathematics

Discrete mathematics: Graph theory

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