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Machine Learning by Sergios Theodoridis

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Chapter 14

Monte Carlo Methods

Abstract

This chapter deals with basic concepts and definitions concerning Monte Carlo sampling techniques. Rejection sampling and importance sampling are first introduced. Markov chains and some of their basic properties are discussed. Then the Metropolis-Hastings and Gibbs algorithms are presented. At the end of the chapter, a case study concerning change point detection is considered.

Keywords

Monte Carlo methods

Rejection sampling

Importance sampling

Markov chains

Metropolis-Hastings algorithm

Gibbs sampling

Change point detection

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