Approximability of Probability Distributions

Part of Advances in Neural Information Processing Systems 16 (NIPS 2003)

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Alina Beygelzimer, Irina Rish


We consider the question of how well a given distribution can be approx- imated with probabilistic graphical models. We introduce a new param- eter, effective treewidth, that captures the degree of approximability as a tradeoff between the accuracy and the complexity of approximation. We present a simple approach to analyzing achievable tradeoffs that ex- ploits the threshold behavior of monotone graph properties, and provide experimental results that support the approach.