# Posteriors, conjugacy, and exponential families for completely random measures

arXiv: https://arxiv.org/abs/1410.6843 · HTML: https://mlsanity.com/paper/1410.6843/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Oct 2014.

## Built on by (most cited first, 3 of 3)

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- [A Nonparametric Bayes Approach to Online Activity Prediction](https://mlsanity.com/paper/2401.14722/) (2024)
- [Improved prediction of future user activity in online A/B testing](https://mlsanity.com/paper/2402.03231/) (2024)

## Abstract (arXiv)

We demonstrate how to calculate posteriors for general CRM-based priors and likelihoods for Bayesian nonparametric models. 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.