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Stochastic Discrete Clenshaw-Curtis Quadrature
2016
International Conference on Machine Learning
The partition function is fundamental for probabilistic graphical models-it is required for inference, parameter estimation, and model selection. Evaluating this function corresponds to discrete integration, namely a weighted sum over an exponentially large set. This task quickly becomes intractable as the dimensionality of the problem increases. We propose an approximation scheme that, for any discrete graphical model whose parameter vector has bounded norm, estimates the partition function
dblp:conf/icml/PiatkowskiM16
fatcat:ilfth6gswjdjxawr74hvn4q6b4