Markov chains and Markov chain Monte Carlo methods

Autor:	Ariadna, Gómez del Pulgar Martínez
Přispěvatelé:	Rovira Escofet, Carles
Jazyk:	angličtina
Rok vydání:	2022
Předmět:	Anàlisi numèrica Monte Carlo method Probabilities Bachelor's thesis Processos de Markov Markov processes Bachelor's theses Probabilitats Treballs de fi de grau Mètode de Montecarlo Numerical analysis
Zdroj:	Dipòsit Digital de la UB Universidad de Barcelona
Popis:	Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2022, Director: Carles Rovira Escofet [en] The aim of this project is to thoroughly study the main properties of discretetime Markov chains with finite state spaces and one of its applications that finds greatest usage, Markov chain Monte Carlo (MCMC) methods, which are simulation tools to estimate integrals and sample from distributions. A brief description of regular Monte Carlo is included to introduce and understand MCMC. Aside from the theoretical description and algorithms, practical considerations to take into account when implementing MCMC, such as the thermalization of chains and determining the number of iterations, are included as well. A simple example of the calculation of $\Gamma(3 / 2)$ is executed so as to illustrate the functioning and performance of MCMC.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=dedup_wf_001::abb311767f1b336480656eea0b9e7f57 http://hdl.handle.net/2445/189765 Zobrazit plný text záznamu