OpenAlex | Posteriors, conjugacy, and exponential families for completely random measures

Posteriors, conjugacy, and exponential families for completely random measures

Work

Year: 2018

Type: article

Abstract: 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... more

Source: Bernoulli

Authors Tamara Broderick, Ashia C. Wilson, Michael I. Jordan

Institutions Massachusetts Institute of Technology, University of California, Berkeley

Cites: 43

Cited by: 21

Related to: 10

FWCI: 2.247

Citation percentile (by year/subfield): 86.46

Topic: Bayesian Methods and Mixture Models

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Sustainable Development Goal Reduced inequalities

Open Access status: bronze