LGAug 28, 2017
Hyperprior on symmetric Dirichlet distribution
arXiv:1708.08177v13 citations
Originality Synthesis-oriented
AI Analysis
This provides a methodological improvement for Bayesian statisticians working with Dirichlet distributions, though it appears incremental in scope.
The authors tackled the problem of placing vague hyperpriors on Dirichlet distributions by developing an adaptive rejection sampling method for parameter updates, and demonstrated its effectiveness through synthetic experiments on over-fitted mixture models.
In this article we introduce how to put vague hyperprior on Dirichlet distribution, and we update the parameter of it by adaptive rejection sampling (ARS). Finally we analyze this hyperprior in an over-fitted mixture model by some synthetic experiments.