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Inference for a class of partially observed point process models

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Title: Inference for a class of partially observed point process models
Authors: Martin, JS
Jasra, A
McCoy, E
Item Type: Journal Article
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.
Issue Date: 1-Jun-2013
Date of Acceptance: 23-May-2012
URI: http://hdl.handle.net/10044/1/64620
DOI: https://dx.doi.org/10.1007/s10463-012-0375-8
ISSN: 0020-3157
Publisher: Springer
Start Page: 413
End Page: 437
Journal / Book Title: Annals of the Institute of Statistical Mathematics
Volume: 65
Issue: 3
Copyright Statement: © 2012 Springer-Verlag. The final publication is available at Springer via https://dx.doi.org/10.1007/s10463-012-0375-8
Keywords: Science & Technology
Physical Sciences
Statistics & Probability
Mathematics
Point processes
Sequential Monte Carlo
Intensity estimation
SEQUENTIAL MONTE-CARLO
STOCHASTIC POISSON PROCESSES
SIMULATION
stat.ME
0104 Statistics
Publication Status: Published
Online Publication Date: 2012-08-30
Appears in Collections:Mathematics
Statistics
Faculty of Natural Sciences



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