- Volume 24, Number 4B (2018), 3181-3221.
Posteriors, conjugacy, and exponential families for completely random measures
We demonstrate how to calculate posteriors for general Bayesian nonparametric priors and likelihoods based on completely random measures (CRMs). We further show how to represent Bayesian nonparametric priors as a sequence of finite draws using a size-biasing approach – and how to represent full Bayesian nonparametric models via finite marginals. Motivated by conjugate priors based on exponential family representations of likelihoods, we introduce a notion of exponential families for CRMs, which we call exponential CRMs. This construction allows us to specify automatic Bayesian nonparametric conjugate priors for exponential CRM likelihoods. We demonstrate that our exponential CRMs allow particularly straightforward recipes for size-biased and marginal representations of Bayesian nonparametric models. Along the way, we prove that the gamma process is a conjugate prior for the Poisson likelihood process and the beta prime process is a conjugate prior for a process we call the odds Bernoulli process. We deliver a size-biased representation of the gamma process and a marginal representation of the gamma process coupled with a Poisson likelihood process.
Bernoulli, Volume 24, Number 4B (2018), 3181-3221.
Received: October 2014
Revised: May 2016
First available in Project Euclid: 18 April 2018
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Broderick, Tamara; Wilson, Ashia C.; Jordan, Michael I. Posteriors, conjugacy, and exponential families for completely random measures. Bernoulli 24 (2018), no. 4B, 3181--3221. doi:10.3150/16-BEJ855. https://projecteuclid.org/euclid.bj/1524038752