Lecture

Theory of MCMC

Description

This lecture covers the theory of Markov Chain Monte Carlo (MCMC) sampling, discussing the process of sampling from a target distribution using a Markov chain with a transition matrix. It explains the convergence conditions towards stationarity, the choice of transition matrix, and the evolution of the target distribution. The lecture also delves into the detailed balance condition, ergodicity, irreducibility, and aperiodicity of the Markov chain.

Instructor

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Official source

https://mediaspace.epfl.ch/media/0_uclk0if2

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Ontological neighbourhood

Mathematics

Analysis: Calculus

Statistics

Data collection: Statistical sampling

Statistical inference: Bayesian inference

Systems science and engineering

Systems theory: Chaos theory

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