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Variational inference for multiplicative intensity models

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Listed:
  • Lau, John W.
  • Cripps, Edward
  • Hui, Wendy

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.

Suggested Citation

  • Lau, John W. & Cripps, Edward & Hui, Wendy, 2020. "Variational inference for multiplicative intensity models," Statistics & Probability Letters, Elsevier, vol. 161(C).
    • Handle: RePEc:eee:stapro:v:161:y:2020:i:c:s0167715220300237
    DOI: 10.1016/j.spl.2020.108720
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    References listed on IDEAS

    as
    1. Ishwaran, Hemant & James, Lancelot F., 2004. "Computational Methods for Multiplicative Intensity Models Using Weighted Gamma Processes: Proportional Hazards, Marked Point Processes, and Panel Count Data," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 175-190, January.
    2. Braun, Michael & McAuliffe, Jon, 2010. "Variational Inference for Large-Scale Models of Discrete Choice," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 324-335.
    3. Lau, John W., 2006. "Bayesian semi-parametric modeling for mixed proportional hazard models with right censoring," Statistics & Probability Letters, Elsevier, vol. 76(7), pages 719-728, April.
    4. Albert Lo & Chung-Sing Weng, 1989. "On a class of Bayesian nonparametric estimates: II. Hazard rate estimates," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 41(2), pages 227-245, June.
    Full references (including those not matched with items on IDEAS)

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