Variational inference for multiplicative intensity models

John W. Lau, Edward Cripps and Wendy Hui

Statistics & Probability Letters, 2020, vol. 161, issue C

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.

Keywords: Variational inference; Multiplicative intensity; Bayesian nonparametrics (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1016/j.spl.2020.108720

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