Imperial College London
Alternate

Dr James S. Martin

Faculty of Natural Sciences, Department of Mathematics

Senior Strategic Teaching Fellow in Data Science
 
 
 
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Contact

 

james.martin

 
 
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Location

 

Roderic Hill BuildingSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Martin:2013:10.1007/s10463-012-0375-8,
author = {Martin, JS and Jasra, A and McCoy, E},
doi = {10.1007/s10463-012-0375-8},
journal = {Annals of the Institute of Statistical Mathematics},
pages = {413--437},
title = {Inference for a class of partially observed point process models},
url = {http://dx.doi.org/10.1007/s10463-012-0375-8},
volume = {65},
year = {2013}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - 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.
AU  - Martin,JS
AU  - Jasra,A
AU  - McCoy,E
DO  - 10.1007/s10463-012-0375-8
EP  - 437
PY  - 2013///
SN  - 0020-3157
SP  - 413
TI  - Inference for a class of partially observed point process models
T2  - Annals of the Institute of Statistical Mathematics
UR  - http://dx.doi.org/10.1007/s10463-012-0375-8
UR  - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000319426200001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR  - http://hdl.handle.net/10044/1/64620
VL  - 65
ER  -
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