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Efficient Bayesian methods for counting processes in partially observable environments

Abstract:

When sensors that count events are unreliable, the data sets that result cannot be trusted. We address this common problem by developing practical Bayesian estimators for a partially observable Poisson process (POPP). Unlike Bayesian estimation for a fully observable Poisson process (FOPP) this is non-trivial, since there is no conjugate density for a POPP and the posterior has a number of elements that grow exponentially in the number of observed intervals. We present two tractable approxima...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Publisher's version

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Engineering Science
Oxford college:
Pembroke College
Role:
Author
ORCID:
0000-0002-7556-6098
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Grant:
FP7/2007-2013 under grant agreement No 600623, STRANDS
Publisher:
Proceedings of Machine Learning Research Publisher's website
Volume:
84
Pages:
1906--1913
Series:
Proceedings of Machine Learning Research
Publication date:
2018-05-04
Acceptance date:
2017-12-22
Pubs id:
pubs:856911
URN:
uri:721fda5a-6de8-4c1a-be64-35c4f4ae3409
UUID:
uuid:721fda5a-6de8-4c1a-be64-35c4f4ae3409
Local pid:
pubs:856911

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