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149
AN IMPROVED MERGESPLIT SAMPLER FOR CONJUGATE DIRICHLET PROCESS MIXTURE MODELS
, 2003
"... The Gibbs sampler is the standard Markov chain Monte Carlo sampler for drawing samples from the posterior distribution of conjugate Dirichlet process mixture models. Researchers have noticed the Gibbs sampler’s tendency to get stuck in local modes and, thus, poorly explore the posterior distribution ..."
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Cited by 28 (2 self)
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The Gibbs sampler is the standard Markov chain Monte Carlo sampler for drawing samples from the posterior distribution of conjugate Dirichlet process mixture models. Researchers have noticed the Gibbs sampler’s tendency to get stuck in local modes and, thus, poorly explore the posterior
A SplitMerge Markov Chain Monte Carlo Procedure for the Dirichlet Process Mixture Model
 Journal of Computational and Graphical Statistics
, 2000
"... . We propose a splitmerge Markov chain algorithm to address the problem of inefficient sampling for conjugate Dirichlet process mixture models. Traditional Markov chain Monte Carlo methods for Bayesian mixture models, such as Gibbs sampling, can become trapped in isolated modes corresponding to an ..."
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Cited by 150 (0 self)
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. We propose a splitmerge Markov chain algorithm to address the problem of inefficient sampling for conjugate Dirichlet process mixture models. Traditional Markov chain Monte Carlo methods for Bayesian mixture models, such as Gibbs sampling, can become trapped in isolated modes corresponding
DirichletMultinomial Model The Dirichlet Distribution as the Canonically Constructed Conjugate Prior
, 2012
"... This short note contains an explicit proof of the Dirichlet distribution being the conjugate prior to the Multinomial sample distribution as resulting from the general construction method described, e.g., in Bernardo and Smith (2000). The wellknown DirichletMultinomial model is thus shown to fit ..."
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This short note contains an explicit proof of the Dirichlet distribution being the conjugate prior to the Multinomial sample distribution as resulting from the general construction method described, e.g., in Bernardo and Smith (2000). The wellknown DirichletMultinomial model is thus shown to fit
DIRICHLET’S UNIT THEOREM
"... Dirichlet’s unit theorem describes the structure of the unit group of any order in a number field. Theorem 1.1 (Dirichlet, 1846). Let K be a number field with r1 real embeddings and 2r2 pairs of complex conjugate embeddings. The unit group of any order in K is finitely ..."
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Dirichlet’s unit theorem describes the structure of the unit group of any order in a number field. Theorem 1.1 (Dirichlet, 1846). Let K be a number field with r1 real embeddings and 2r2 pairs of complex conjugate embeddings. The unit group of any order in K is finitely
Retrospective Markov chain Monte Carlo methods for Dirichlet process hierarchical models
 PROC. IEEE
, 2008
"... Inference for Dirichlet process hierarchical models is typically performed using Markov chain Monte Carlo methods, which can be roughly categorised into marginal and conditional methods. The former integrate out analytically the infinitedimensional component of the hierarchical model and sample fro ..."
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Cited by 84 (5 self)
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show how the algorithms can obtain samples from functionals of the Dirichlet process. The marginal and the conditional methods are compared and a careful simulation study is included, which involves a nonconjugate model, different datasets and prior specifications.
Modelbased clustering for expression data via a Dirichlet process mixture model,” in Bayesian Inference for Gene Expression and Proteomics
, 2006
"... This chapter describes a clustering procedure for microarray expression data based on a welldefined statistical model, specifically, a conjugate Dirichlet process mixture model. The clustering algorithm groups genes whose latent variables governing expression are equal, that is, genes belonging to ..."
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Cited by 37 (0 self)
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This chapter describes a clustering procedure for microarray expression data based on a welldefined statistical model, specifically, a conjugate Dirichlet process mixture model. The clustering algorithm groups genes whose latent variables governing expression are equal, that is, genes belonging
A Generalization of the Dirichlet Distribution
"... This vignette discusses a generalization of the Dirichlet distribution, the ‘hyperdirichlet’, in which various types of incomplete observations may be incorporated. It is conjugate to the multinomial distribution when some observations are censored or grouped. The hyperdirichlet R package is introdu ..."
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Cited by 5 (0 self)
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This vignette discusses a generalization of the Dirichlet distribution, the ‘hyperdirichlet’, in which various types of incomplete observations may be incorporated. It is conjugate to the multinomial distribution when some observations are censored or grouped. The hyperdirichlet R package
Dirichlet to Neumann operator on differential forms
 Bull. Sci. Math
"... We define the Dirichlet to Neumann operator on exterior differential forms for a compact Riemannian manifold with boundary and prove that the real additive cohomology structure of the manifold is determined by the DN operator. In particular, an explicit formula is obtained which expresses Betti nu ..."
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Cited by 14 (1 self)
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We define the Dirichlet to Neumann operator on exterior differential forms for a compact Riemannian manifold with boundary and prove that the real additive cohomology structure of the manifold is determined by the DN operator. In particular, an explicit formula is obtained which expresses Betti
Application of MultinomialDirichlet Conjugate in MCMC Estimation : A Breast Cancer Study
, 2010
"... Abstract Studies have been made to investigate the familial risk of breast cancer based on a large casecontrol study and conclude that a small number of affected cases were due to the presence of a rare autosomal dominant allele, where as a larger number of cases reported were non genetic ..."
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Abstract Studies have been made to investigate the familial risk of breast cancer based on a large casecontrol study and conclude that a small number of affected cases were due to the presence of a rare autosomal dominant allele, where as a larger number of cases reported were non genetic
SequentiallyAllocated MergeSplit Sampler for Conjugate and Nonconjugate Dirichlet Process Mixture Models
, 2005
"... This paper proposes a new efficient mergesplit sampler for both conjugate and nonconjugate Dirichlet process mixture (DPM) models. These Bayesian nonparametric models are usually fit using Markov chain Monte Carlo (MCMC) or sequential importance sampling (SIS). The latest generation of Gibbs and Gi ..."
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Cited by 25 (1 self)
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This paper proposes a new efficient mergesplit sampler for both conjugate and nonconjugate Dirichlet process mixture (DPM) models. These Bayesian nonparametric models are usually fit using Markov chain Monte Carlo (MCMC) or sequential importance sampling (SIS). The latest generation of Gibbs
Results 1  10
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149