Inference for a class of partially observed point process models

James S. Martin, Ajay Jasra, Emma McCoy

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

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.
Original languageEnglish (US)
JournalAnnals of the Institute of Statistical Mathematics
Volume65
Issue number3
DOIs
StatePublished - Jun 1 2013
Externally publishedYes

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