Mixture model fitting using conditional models and modal Gibbs sampling

27 Dec 2017Virgilio Gomez-Rubio

In this paper, we present a novel approach to fitting mixture models based on estimating first the posterior distribution of the auxiliary variables that assign each observation to a group in the mixture. The posterior distributions of the remainder of the parameters in the mixture is obtained by averaging over their conditional posterior marginals on the auxiliary variables using Bayesian model averaging... (read more)

PDF Abstract


No code implementations yet. Submit your code now


Results from the Paper

  Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers.

Methods used in the Paper

🤖 No Methods Found Help the community by adding them if they're not listed; e.g. Deep Residual Learning for Image Recognition uses ResNet