Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/122069
Title: Probabilistic programming: the Monte Carlo boost
Author: Gutiérrez Galopa, Alejandro
Director/Tutor: Vitrià i Marca, Jordi
Keywords: Mètode de Montecarlo
Treballs de fi de grau
Processos de Markov
Algorismes computacionals
Programació (Ordinadors)
Basquetbol
Monte Carlo method
Bachelor's thesis
Markov processes
Computer algorithms
Computer programming
Basketball
Issue Date: 27-Jun-2017
Abstract: [en] The main goal of this project is to demonstrate the existing symbiosis between Monte Carlo Markov Chain (MCMC) methods and Probabilistic Programming. By carrying out a thorough study of how MCMC methods work, including an analysis of their algorithms and convergence, the in and outs of Probabilistic Programming can be better understood. One of the many applications of Probabilistic Programming is then employed in the study of the performance of a professional basketball player during his career. The model has been implemented using PyMC3, a Python package for sampling data using Monte Carlo Markov Chain methods.
Note: Treballs Finals de Grau de Matemàtiques, Facultat de Matemàtiques, Universitat de Barcelona, Any: 2017, Director: Jordi Vitrià i Marca
URI: http://hdl.handle.net/2445/122069
Appears in Collections:Treballs Finals de Grau (TFG) - Matemàtiques

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