Variational inference for multiplicative intensity models

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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
Publication statusPublished - 1 Jun 2020


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