A Kernelized Stein Discrepancy for Goodness-of-fit Tests and Model Evaluation

10 Feb 2016Qiang LiuJason D. LeeMichael I. Jordan

We derive a new discrepancy statistic for measuring differences between two probability distributions based on combining Stein's identity with the reproducing kernel Hilbert space theory. We apply our result to test how well a probabilistic model fits a set of observations, and derive a new class of powerful goodness-of-fit tests that are widely applicable for complex and high dimensional distributions, even for those with computationally intractable normalization constants... (read more)

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