资源分类
》 资源 》论文》A Kernelized Stein Discrepancy for Goodness-of-fit Tests

A Kernelized Stein Discrepancy for Goodness-of-fit Tests

2020-03-05 | |  57 |   31 |   0

Abstract

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. Both theoretical and empirical properties of our methods are studied thoroughly.

上一篇：Sparse Nonlinear Regression: Parameter Estimation under Nonconvexity

下一篇：Diversity-Promoting Bayesian Learning of Latent Variable Models

用户评价
全部评价

热门资源

  • Learning to Predi...

    Much of model-based reinforcement learning invo...

  • Stratified Strate...

    In this paper we introduce Stratified Strategy ...

  • The Variational S...

    Unlike traditional images which do not offer in...

  • A Mathematical Mo...

    Direct democracy, where each voter casts one vo...

  • Rating-Boosted La...

    The performance of a recommendation system reli...