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An Efficient Split-Merge MCMC Algorithm for MixtureModels with an Unknown Number of ComponentsErlandson Ferreira Saraiva ∗Departamento de Estatística, Universidade Federal de São CarlosLuís Aparecido MilanDepartamento de Estatística, Universidade Federal de São CarlosFrancisco Louzada-NetoDepartamento de Estatística, Universidade Federal de São CarlosWe propose a new split-merge MCMC algorithm for estimation of mixture models with an unknownnumber of components. The strategy for splitting is based on data and posterior distribution. Allocationprobabilities are calculated based on component parameters which are generated from the posterior distributiongiven the previously allocated observations. The split-merge proposals allows a major changein configuration of the latent variables in a single iteration of the algorithm, avoiding possible localmodes, and are accepted according to the Metropolis-Hastings probability. As advantages, our approachdetermines a quick split proposal, in contrary to former split procedures which require substantial computationaleffort, and remove the need to specify a transition function and consequently the calculate ofthe jacobian, turning the probability of acceptance easier of compute and simplifying the implementation.The performance of the method is verified using an artificial data set and two real data sets. Thefirst real data set consist of benchmark data of velocities from distant galaxies diverging from our ownwhile the second is Escherichia Coli bacterium gene expression data.∗ Apresentador/Speaker35
Bayesian Beta Dynamic Model and ApplicationsCibele Queiroz da Silva ∗Department of Statistics - University of BrasíliaHelio dos Santos MigonDepartment of Statistics - Federal University of Rio de JaneiroLeandro Tavares CorreiaDepartment of Statistics - University of BrasíliaThe beta distribution provides a useful tool for modeling data restricted to the interval (0,1) suchas rates, percentages and proportions. In particular, one may be interested in modelling fluctuations invariables such as the proportion of a given fish species in a lake over time, the monthly unemploymentrates of a given country or the proportion of a given component in compositional data analysis atincreasing depths. We develop a Bayesian Beta Dynamic Model for modelling and forecasting singletime series of proportions. This work is related to the class of the so called Dynamic GeneralizedLinear Models (DGLM). We use non-conjugate priors and some forms of approximate Bayesian analysis,including Linear Bayesian Estimation. Some applications to both real and simulated data are provided.∗ Apresentador/Speaker36
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Page 23 and 24: Bayesian Computing with INLAHåvard
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Page 31 and 32: Partitioning Diversity Measure for
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Page 57 and 58: Aplicação do Algoritmo MCMC de Sa
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Page 63 and 64: Aplicações de Inferência Bayesia
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Page 67 and 68: Accounting for Latent Spatio-Tempor
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Page 71 and 72: A Bayesian Analysis for the General
Page 73 and 74: Comparação de um Modelo Dinâmico
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Estimadores Lineares Bayesianos em
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Modelos de Volatilidade Estocástic
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Processo de Difusão Multivariado c
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Generalized Latent Factor Models fo
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Measuring Vulnerability via Spatial
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Medidas do Valor Preditivo de um Mo
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Aproximações Analíticas para Pos
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Modelo de Risco Logístico Dependen
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A Practical Approach to Elicit Mult
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Modelos Dinâmicos para Deformaçã
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A Semiparametric Bayesian Approach
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Modelo Weibull Modificado de Longa
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Abordagem Bayesiana para Modelos GA
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Modelo Bayesiano Geral de Classe La
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Métodos de Estimação em Modelos
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LASSO Bayesiano no Mapeamento de QT
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Estudo das Matrizes de Covariância
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Sensibilidade da Inferência Bayesi
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Análise Bayesiana para o Estudo de
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Uma Abordagem Bayesiana para Modelo
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Construção de Tábuas de Mortalid
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Um Procedimento Bayesiano para a An
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Um Novo Modelo para Dependência Es
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Distribuição Geométrica Exponenc
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Morbidade Pulmonar e Condições Cl
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Standard Setting for a Rasch Poisso
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Modelo Logístico Misto com Classes
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Metodologia Bayesiana para Detecç
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Modelos Espaço-Temporais para Dado
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Comparação de Modelos para a Iden
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Modelos Multivariados de Regressão
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Estimação Bayesiana em Modelos de
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Previsão de Resultados de Jogos de
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Uma Abordagem Bayesiana para Modelo
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Análise Bayesiana do Modelo Log-Bi
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Desempate TécnicoFilipe Jaeger Zab
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10 o Encontro Brasileiro de Estatí
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