A Comparison of Two Methods for Obtaining a Collective Posterior Distribution

Rafael Catoia Pulgrossi, Natalia Lombardi Oliveira, Adriano Polpo, Rafael Izbicki

Research output: Chapter in Book/Conference paperConference paper

Abstract

Bayesian inference is a powerful method that allows individuals to update their knowledge about any phenomenon when more information about it becomes available. In this paradigm, before data is observed, an individual expresses his uncertainty about the phenomenon of interest through a prior probability distribution. Then, after data is observed, this distribution is updated using Bayes theorem. In many situations, however, one desires to evaluate the knowledge of a group rather than of a single individual. In this case, a way to combine information from different sources is by mixing their uncertainty. The mixture can be done in two ways: before or after the data is observed. Although in both cases, we achieve a collective posterior distribution, they can be substantially different. In this work, we present several comparisons between these two approaches with noninformative priors and use the Kullback–Leibler’s divergence to quantify the amount of information that is gained by each collective distribution.

Original languageEnglish
Title of host publicationBayesian Inference and Maximum Entropy Methods in Science and Engineering - MaxEnt 37, 2017
EditorsAdriano Polpo, Julio Stern, Francisco Louzada, Rafael Izbicki, Hellinton Takada
Place of PublicationCham, Switzerland
PublisherSpringer
Pages221-230
Number of pages10
Volume239
ISBN (Electronic)978-3-319-91143-4
ISBN (Print)9783319911427
DOIs
Publication statusPublished - 1 Jan 2018
Externally publishedYes
Event37th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, MaxEnt 2017 - Paradies Resort Hotel, Jarinu, Brazil
Duration: 9 Jul 201714 Jul 2017
http://inspirehep.net/record/1642773

Publication series

NameSpringer Proceedings in Methematics and Statistics
Volume239

Conference

Conference37th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering, MaxEnt 2017
Abbreviated titleMaxEnt 2017
CountryBrazil
CityJarinu
Period9/07/1714/07/17
Internet address

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