Mcmc Algorithms
Mostrando 1-5 de 5 artigos, teses e dissertações.
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1. Modelo de mistura com número de componentes desconhecido: estimação via método split-merge
We propose the split-merge MCMC and birth-split-merge MCMC algorithms to analyse mixture models with an unknown number of components. The strategy for splitting is based on data and posterior distribution. Allocation probabilities are calculated based on component parameters which are generated from the posterior distribution given the previously allocated o
Publicado em: 2009
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2. MÃtodo adaptativo de Markov Chain Monte Carlo para manipulaÃÃo de modelos Bayesianos
Historically, Bayesian models have deserved special attention from academy and applied fields mainly by allowing mathematical combination of human judgments and empirical data. Markov Chain Monte Carlo (MCMC) methodology is one of the main classes of approaches for computing marginal estimates from Bayesian models. Among Markov Chain Monte Carlo methods, Met
Publicado em: 2009
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3. Modelos longitudinais de grupos múltiplos multiníveis na teoria da resposta ao item: métodos de estimação e seleção estrutural sob uma perspectiva bayesiana / Longitudinal multiple groups multilevel models in the item response theory : estimation methods and structural selection under a bayesian perspective
No presente trabalho propomos uma estrutura bayesiana, através de um esquema de dados aumentados, para analisar modelos longitudinais com grupos mútiplos (MLGMTRI) na Teoria da Resposta ao Item (TRI). Tal estrutura consiste na tríade : modelagem, métodos de estimação e métodos de diagnóstico para a classe de MLGMTRI. Na parte de modelagem, explorou-s
Publicado em: 2008
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4. BAYESIAN LEARNING FOR NEURAL NETWORKS / APRENDIZADO BAYESIANO PARA REDES NEURAIS
This dissertation investigates the Bayesianan Neural Networks, which is a new approach that merges the potencial of the artificial neural networks with the robust analytical analysis of the Bayesian Statistic. Typically, theconventional neural networks such as backpropagation, have good performance but presents problems of convergence, when enough data for t
Publicado em: 1999
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5. Mapping-Linked Quantitative Trait Loci Using Bayesian Analysis and Markov Chain Monte Carlo Algorithms
A Bayesian method for mapping linked quantitative trait loci (QTL) using multiple linked genetic markers is presented. Parameter estimation and hypothesis testing was implemented via Markov chain Monte Carlo (MCMC) algorithms. Parameters included were allele frequencies and substitution effects for two biallelic QTL, map positions of the QTL and markers, all