MAS: a multiplicative approximation scheme for probabilistic inference

NeurIPS 2008 Ydo WexlerChristopher Meek

We propose a multiplicative approximation scheme (MAS) for inference problems in graphical models, which can be applied to various inference algorithms. The method uses $\epsilon$-decompositions which decompose functions used throughout the inference procedure into functions over smaller sets of variables with a known error $\epsilon$... (read more)

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