_:n299b451a1b6c48cf87e27db12d75df1fxb34
<http://schema.org/description>
"In this paper, we present a novel approach to fitting mixture models based on\nestimating first the posterior distribution of the auxiliary variables that\nassign each observation to a group in the mixture. The posterior distributions\nof the remainder of the parameters in the mixture is obtained by averaging over\ntheir conditional posterior marginals on the auxiliary variables using Bayesian\nmodel averaging.\n  A new algorithm based on Gibbs sampling is used to approximate the posterior\ndistribution of the auxiliary variables without sampling any other parameter in\nthe model. In particular, the modes of the full conditionals of the parameters\nof the densities in the mixture are computed and these are plugged-in to the\nfull conditional of the auxiliary variables to draw samples. This\napproximation, that we have called 'modal' Gibbs sampling, reduces the\ncomputational burden in the Gibbs sampling algorithm and still provides very\ngood estimates of the posterior distribution of the auxiliary variables.\nConditional models on the auxiliary variables are fitted using the Integrated\nNested Laplace Approximation (INLA) to obtain the conditional posterior\ndistributions, including modes, of the distributional parameters in the\nmixtures.\n  This approach is general enough to consider mixture models with discrete or\ncontinuous outcomes from a wide range of distributions and latent models as\nconditional model fitting is done with INLA. This presents several other\nadvantages, such as fast fitting of the conditional models, not being\nrestricted to the use of conjugate priors on the model parameters and being\nless prone to label switching. Within this framework, computing the marginal\nlikelihood of the mixture model when the number of groups in the mixture is\nknown is easy and it can be used to tackle selection of the number of\ncomponents."
<https://api.deepai.org/profile/virgilio-gomez-rubio>