Particle Gibbs split-merge sampling for Bayesian inference in mixture models
This paper presents an original Markov chain Monte Carlo method to sample from the posterior distribution of conjugate mixture models. This algorithm relies on a flexible split-merge procedure built using the particle Gibbs sampler introduced in Andrieu et al. (2009, 2010). The resulting so-called P...
| Main Authors: | , , |
|---|---|
| Format: | Journal article |
| Published: |
Journal of Machine Learning Research
2017
|