Variational inference for multiplicative intensity models

Research output: Contribution to journalArticle

Abstract

We extend variational inference approximation of probability density functions to multiplicative intensity functions. For Bayesian nonparametrics, this provides a computationally efficient alternative to the blocked Gibbs sampler described in Ishwaran and James (2004). Simulation results are presented to demonstrate performance.

Original languageEnglish
Article number108720
JournalStatistics and Probability Letters
Volume161
DOIs
Publication statusPublished - 1 Jun 2020

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