Predictive Classification and Bayesian Inference

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Title: Predictive Classification and Bayesian Inference
Author: Xiong, Jie
Contributor organization: University of Helsinki, Faculty of Science, Department of Mathematics and Statistics
Helsingin yliopisto, matemaattis-luonnontieteellinen tiedekunta, matematiikan ja tilastotieteen laitos
Helsingfors universitet, matematisk-naturvetenskapliga fakulteten, institutionen för matematik och statistik
Publisher: Helsingin yliopisto
Date: 2015-06-15
Language: eng
Thesis level: Doctoral dissertation (article-based)
Abstract: A general inductive probabilistic framework for clustering and classification is introduced using the principles of Bayesian predictive inference, such that all quantities are jointly modelled and the uncertainty is fully acknowledged through the posterior predictive distribution. Several learning rules have been considered and the theoretical results are extended to acknowledge complex dependencies within the datasets. Multiple probabilistic models have been developed for analysing data from a wide variate of fields of application. State-of-art algorithms are introduced and developed for the model optimization.
Subject: statistics
Rights: Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.

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