Hernández-González, JerónimoCerquides Bueno, JesúsPadrós Zamora, Àlex2022-05-242022-05-242021-06-30https://hdl.handle.net/2445/185907Treballs finals del Màster de Fonaments de Ciència de Dades, Facultat de matemàtiques, Universitat de Barcelona. Curs: 2020-2021. Tutor: Jerónimo Hernández González i Jesús Cerquides Bueno[en] In this Master Thesis we study some classical approaches for crowd annotation models such as the pooled multinomial model or the Dawid-Skene models. These models try to learn from the crowd, which is not required to be composed of experts. In particular, the problem of label aggregation that we deal with can be seen as a probabilistic graphical model. We propose an algorithm that aims to solve the problem of label-switching for generic inference platforms such as STAN without any previous intervention to the optimization/sampling method. We also study its performance by means of the Kullback-Leibler divergence, where we see that the results are better after applying our proposed correction.45 p.application/pdfengcc-by-nc-nd (c) Àlex Padrós Zamora, 2021codi: GPL (c) Àlex Padrós Zamora, 2021http://creativecommons.org/licenses/by-nc-nd/3.0/es/http://www.gnu.org/licenses/gpl-3.0.ca.htmlDades massivesAlgorismes computacionalsAprenentatge automàticTreballs de fi de màsterBig dataComputer algorithmsMachine learningMaster's thesesinfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/openAccess