Efficient Bayesian methods for counting processes in partially observable environments
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 n...
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Proceedings of Machine Learning Research
2018
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