3 research outputs found
A Kernel Stein Test of Goodness of Fit for Sequential Models
We propose a goodness-of-fit measure for probability densities modeling observations with varying dimensionality, such as text documents of differing lengths or variable-length sequences. The
proposed measure is an instance of the kernel
Stein discrepancy (KSD), which has been used
to construct goodness-of-fit tests for unnormalized densities. The KSD is defined by its Stein
operator: current operators used in testing apply to fixed-dimensional spaces. As our main
contribution, we extend the KSD to the variabledimension setting by identifying appropriate Stein
operators, and propose a novel KSD goodness-offit test. As with the previous variants, the proposed KSD does not require the density to be normalized, allowing the evaluation of a large class
of models. Our test is shown to perform well in
practice on discrete sequential data benchmarks