Hawes, Nick

Jovan, Ferdian

Wyatt, Jeremy

When sensors that count events are unre- liable, 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 approximations, which we combine in a switching filter. This switching filter enables efficient and accurate estimation of the posterior. We perform a detailed empirical analysis, using both simulated and real-world data

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Efficient bayesian methods for counting processes in partially observable environments

http://pure-oai.bham.ac.uk/ws/files/47696503/Efficient_Bayesian.pdf

Efficient bayesian methods for counting processes in partially observable environments

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University of Birmingham Research Portal