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• W2268305557 abstract "Performing efficient inference on Bayesian Networks (BNs), with large numbers of densely connected variables is challenging. With exact inference methods, such as the Junction Tree algorithm, clustering complexity can grow exponentially with the number of nodes and so computation becomes intractable. This paper presents a general purpose approximate inference algorithm called Triplet Region Construction (TRC) that reduces the clustering complexity for factorized models from worst case exponential to polynomial. We employ graph factorization to reduce connection complexity and produce clusters of limited size. Unlike MCMC algorithms TRC is guaranteed to converge and we present experiments that show that TRC achieves accurate results when compared with exact solutions." @default.
• W2268305557 created "2016-06-24" @default.
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• W2268305557 date "2016-02-05" @default.
• W2268305557 modified "2023-10-16" @default.
• W2268305557 title "Region Based Approximation for High Dimensional Bayesian Network Models" @default.
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• W2268305557 doi "https://doi.org/10.48550/arxiv.1602.02086" @default.
• W2268305557 hasPublicationYear "2016" @default.
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