Approximate Inference in Graphical Models using Tensor Decompositions
نویسنده
چکیده
We demonstrate that tensor decompositions can be used to transform graphical models into structurally simpler graphical models that approximate the same joint probability distribution. In this way, standard inference algorithms such as the junction tree algorithm, can be used in order to use the transformed graphical model for approximate inference. The usefulness of the technique is demonstrated by means of its application to thirty randomly generated small-world Markov networks.
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تاریخ انتشار 2007