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.
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Acknowledgments
We thank Nick Whiteley for conversations on this work. The first author acknowledges the support of an EPSRC grant. The second author was supported by an MOE grant. We thank two referees and an associate editor for their comments, which have vastly improved the article.
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Martin, J.S., Jasra, A. & McCoy, E. Inference for a class of partially observed point process models. Ann Inst Stat Math 65, 413–437 (2013). https://doi.org/10.1007/s10463-012-0375-8
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DOI: https://doi.org/10.1007/s10463-012-0375-8