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• W2153074847 abstract "We present a new approach to inference in Bayesian networks, which is based on representing the network using a polynomial and then retrieving answers to probabilistic queries by evaluating and differentiating the polynomial. The network polynomial itself is exponential in size, but we show how it can be computed efficiently using an arithmetic circuit that can be evaluated and differentiated in time and space linear in the circuit size. The proposed framework for inference subsumes one of the most influential methods for inference in Bayesian networks, known as the tree-clustering or jointree method, which provides a deeper understanding of this classical method and lifts its desirable characteristics to a much more general setting. We discuss some theoretical and practical implications of this subsumption." @default.
• W2153074847 created "2016-06-24" @default.
• W2153074847 creator A5027252816 @default.
• W2153074847 date "2003-05-01" @default.
• W2153074847 modified "2023-09-30" @default.
• W2153074847 title "A differential approach to inference in Bayesian networks" @default.
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• W2153074847 doi "https://doi.org/10.1145/765568.765570" @default.
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