Variational inference for multiplicative intensity models

John W. LAU, Edward CRIPPS, Wendy HUI

Research output: Other PublicationsOther ArticleCommunication

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
Pages108720
Volume161
Specialist publicationStatistics and Probability Letters
DOIs
Publication statusE-pub ahead of print - 4 Feb 2020

Fingerprint

Multiplicative Functions
Bayesian Nonparametrics
Intensity Function
Gibbs Sampler
Probability density function
Multiplicative
Alternatives
Approximation
Demonstrate
Simulation
Model

Keywords

  • Variational inference
  • Multiplicative intensity
  • Bayesian nonparametrics

Cite this

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title = "Variational inference for multiplicative intensity models",
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",
author = "LAU, {John W.} and Edward CRIPPS and Wendy HUI",
year = "2020",
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language = "English",
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journal = "Statistics and Probability Letters",
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Variational inference for multiplicative intensity models. / LAU, John W.; CRIPPS, Edward; HUI, Wendy.

In: Statistics and Probability Letters, Vol. 161, 06.2020, p. 108720.

Research output: Other PublicationsOther ArticleCommunication

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AB - 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.

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SP - 108720

JO - Statistics and Probability Letters

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