We demonstrate how to perform direct simulation for discrete mixture models. The approach is based on directly calculating the posterior distribution using a set of recursions which are similar to those of the Forward-Backward algorithm. Our approach is more practicable than existing perfect simulation methods for mixtures. For example, we analyse 1096 observations from a 2 component Poisson mixture, and 240 observations under a 3 component Poisson mixture (with unknown mixture proportions and Poisson means in each case). Simulating samples of 10,000 perfect realisations took about 17 minutes and an hour respectively on a 900 MHz ultraSPARC computer. Our method can also be used to perform perfect simulation from Markov-dependent mixture mod...
This dissertation explores a Bayesian nonparametric approach to mixture modeling and the use of the ...
Abstract only:\ud \ud Today’s data analysts and modellers are in the luxurious position of being abl...
This book provides a general theoretical background for constructing the recursive Bayesian estimati...
We propose and develop a novel and effective perfect sampling methodology for simulating from poster...
A natural Bayesian approach for mixture models with an unknown number of com-ponents is to take the ...
In this paper we present an application of the read-once coupling from the past algorithm to problem...
We present a method of generating random vectors from a distribution having an absolutely continuous...
In this paper we present an application of read-once cou-pling from the past to problems in Bayesian...
International audienceThis chapter surveys the most standard Monte Carlo methods available for simul...
Mixture distributions and models are useful methods of describing data that cannot be estimated with...
There are two open problems when finite mixture densities are used to model multivariate data: the s...
Bayesian methods are often optimal, yet increasing pressure for fast computations, especially with s...
In this paper we present an application of the read-once coupling from the past algorithm to problem...
We propose a more efficient version of the slice sampler for Dirichlet process mixture models descri...
A new method is proposed to generate sample Gaussian mixture distributions according to prespecified...
This dissertation explores a Bayesian nonparametric approach to mixture modeling and the use of the ...
Abstract only:\ud \ud Today’s data analysts and modellers are in the luxurious position of being abl...
This book provides a general theoretical background for constructing the recursive Bayesian estimati...
We propose and develop a novel and effective perfect sampling methodology for simulating from poster...
A natural Bayesian approach for mixture models with an unknown number of com-ponents is to take the ...
In this paper we present an application of the read-once coupling from the past algorithm to problem...
We present a method of generating random vectors from a distribution having an absolutely continuous...
In this paper we present an application of read-once cou-pling from the past to problems in Bayesian...
International audienceThis chapter surveys the most standard Monte Carlo methods available for simul...
Mixture distributions and models are useful methods of describing data that cannot be estimated with...
There are two open problems when finite mixture densities are used to model multivariate data: the s...
Bayesian methods are often optimal, yet increasing pressure for fast computations, especially with s...
In this paper we present an application of the read-once coupling from the past algorithm to problem...
We propose a more efficient version of the slice sampler for Dirichlet process mixture models descri...
A new method is proposed to generate sample Gaussian mixture distributions according to prespecified...
This dissertation explores a Bayesian nonparametric approach to mixture modeling and the use of the ...
Abstract only:\ud \ud Today’s data analysts and modellers are in the luxurious position of being abl...
This book provides a general theoretical background for constructing the recursive Bayesian estimati...