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Systems and methods for tractable variational approximation for inference in decision-graph bayesian networks

机译:决策图贝叶斯网络中用于推理的易处理变分近似的系统和方法

摘要

The present invention leverages approximations of distributions to provide tractable variational approximations, based on at least one continuous variable, for inference utilization in Bayesian networks where local distributions are decision-graphs. These tractable approximations are employed in lieu of exact inferences that are normally NP-hard to solve. By utilizing Jensen's inequality applied to logarithmic distributions composed of a generalized sum including an introduced arbitrary conditional distribution, a means is acquired to resolve a tightly bound likelihood distribution. The means includes application of Mean-Field Theory, approximations of conditional probability distributions, and/or other means that allow for a tractable variational approximation to be achieved.
机译:本发明基于至少一个连续变量,利用分布的近似来提供易于处理的变分近似,以用于在以局部分布为决策图的贝叶斯网络中进行推理。这些易于处理的近似用来代替通常难以解决的精确推论。通过利用詹森不等式应用于由包括和引入的任意条件分布的广义和组成的对数分布,获得了一种解决紧迫似然分布的方法。该手段包括均值场理论的应用,条件概率分布的近似和/或允许实现易处理的变分近似的其他手段。

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