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Please use this identifier to cite or link to this item: https://hdl.handle.net/2445/189765
Markov chains and Markov chain Monte Carlo methods
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[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.
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Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2022, Director: Carles Rovira Escofet
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ARIADNA, Gómez del Pulgar Martínez. Markov chains and Markov chain Monte Carlo methods. [consulted: 12 of October of 2026]. Available at: https://hdl.handle.net/2445/189765