Variational inference for multiplicative intensity models

John W. LAU; Edward CRIPPS; Wendy HUI

doi:10.1016/j.spl.2020.108720

Variational inference for multiplicative intensity models

John W. LAU, Edward CRIPPS, Wendy HUI

Department of Computing and Decision Sciences

Research output: Other Publications › Other Article › Communication

2 Citations (Scopus)

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 language	English
Pages	108720
Volume	161
Specialist publication	Statistics and Probability Letters
DOIs	https://doi.org/10.1016/j.spl.2020.108720
Publication status	Published - Jun 2020

Keywords

Variational inference
Multiplicative intensity
Bayesian nonparametrics

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10.1016/j.spl.2020.108720

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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",

month = jun,

doi = "10.1016/j.spl.2020.108720",

language = "English",

volume = "161",

pages = "108720",

journal = "Statistics and Probability Letters",

issn = "0167-7152",

publisher = "Elsevier B.V.",

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T1 - Variational inference for multiplicative intensity models

AU - LAU, John W.

AU - CRIPPS, Edward

AU - HUI, Wendy

PY - 2020/6

Y1 - 2020/6

N2 - 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.

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.

KW - Variational inference

KW - Multiplicative intensity

KW - Bayesian nonparametrics

UR - https://www.scopus.com/pages/publications/85079409837

U2 - 10.1016/j.spl.2020.108720

DO - 10.1016/j.spl.2020.108720

M3 - Other Article

SN - 0167-7152

VL - 161

SP - 108720

JO - Statistics and Probability Letters

JF - Statistics and Probability Letters

ER -

Variational inference for multiplicative intensity models

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Keywords

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