Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian Networks
نویسندگان
چکیده
منابع مشابه
Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian Networks
Dynamic Bayesian networks (DBNs) are a general model for stochastic processes with partially observed states. Belief filtering in DBNs is the task of inferring the belief state (i.e. the probability distribution over process states) based on incomplete and uncertain observations. In this article, we explore the idea of accelerating the filtering task by automatically exploiting causality in the...
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Dynamic Bayesian networks (DBNs) are a general model for stochastic processes with partially observed states. Belief filtering in DBNs is the task of inferring the belief state (i.e. the probability distribution over process states) based on incomplete and uncertain observations. In this article, we explore the idea of accelerating the filtering task by automatically exploiting causality in the...
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ژورنال
عنوان ژورنال: Journal of Artificial Intelligence Research
سال: 2016
ISSN: 1076-9757
DOI: 10.1613/jair.5044