Please use this identifier to cite or link to this item: https://doi.org/10.1007/s10463-012-0375-8
Title: Inference for a class of partially observed point process models
Authors: Martin, J.S.
Jasra, A. 
McCoy, E.
Keywords: Intensity estimation
Point processes
Sequential Monte Carlo
Issue Date: Jun-2013
Citation: Martin, J.S., Jasra, A., McCoy, E. (2013-06). Inference for a class of partially observed point process models. Annals of the Institute of Statistical Mathematics 65 (3) : 413-437. ScholarBank@NUS Repository. https://doi.org/10.1007/s10463-012-0375-8
Abstract: This paper presents a simulation-based framework for sequential inference from partially and discretely observed point process models with static parameters. Taking on a Bayesian perspective for the static parameters, we build upon sequential Monte Carlo methods, investigating the problems of performing sequential filtering and smoothing in complex examples, where current methods often fail. We consider various approaches for approximating posterior distributions using SMC. Our approaches, with some theoretical discussion are illustrated on a doubly stochastic point process applied in the context of finance. © 2012 The Institute of Statistical Mathematics, Tokyo.
Source Title: Annals of the Institute of Statistical Mathematics
URI: http://scholarbank.nus.edu.sg/handle/10635/125055
ISSN: 00203157
DOI: 10.1007/s10463-012-0375-8
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