14 research outputs found
A Kernel Stein Test for Comparing Latent Variable Models
We propose a kernel-based nonparametric test of relative goodness of fit,
where the goal is to compare two models, both of which may have unobserved
latent variables, such that the marginal distribution of the observed variables
is intractable. The proposed test generalises the recently proposed kernel
Stein discrepancy (KSD) tests (Liu et al., 2016, Chwialkowski et al., 2016,
Yang et al., 2018) to the case of latent variable models, a much more general
class than the fully observed models treated previously. As our main
theoretical contribution, we prove that the new test, with a properly
calibrated threshold, has a well-controlled type-I error. In the case of models
with low-dimensional latent structure and high-dimensional observations, our
test significantly outperforms the relative Maximum Mean Discrepancy test,
which cannot exploit the latent structure.Comment: update test statistic (MCMC version
Stochastic Stein Discrepancies
Stein discrepancies (SDs) monitor convergence and non-convergence in
approximate inference when exact integration and sampling are intractable.
However, the computation of a Stein discrepancy can be prohibitive if the Stein
operator - often a sum over likelihood terms or potentials - is expensive to
evaluate. To address this deficiency, we show that stochastic Stein
discrepancies (SSDs) based on subsampled approximations of the Stein operator
inherit the convergence control properties of standard SDs with probability 1.
In our experiments with biased Markov chain Monte Carlo (MCMC) hyperparameter
tuning, approximate MCMC sampler selection, and stochastic Stein variational
gradient descent, SSDs deliver comparable inferences to standard SDs with
orders of magnitude fewer likelihood evaluations
Testing Goodness of Fit of Conditional Density Models with Kernels
We propose two nonparametric statistical tests of goodness of fit for
conditional distributions: given a conditional probability density function
and a joint sample, decide whether the sample is drawn from
for some density . Our tests, formulated with a Stein
operator, can be applied to any differentiable conditional density model, and
require no knowledge of the normalizing constant. We show that 1) our tests are
consistent against any fixed alternative conditional model; 2) the statistics
can be estimated easily, requiring no density estimation as an intermediate
step; and 3) our second test offers an interpretable test result providing
insight on where the conditional model does not fit well in the domain of the
covariate. We demonstrate the interpretability of our test on a task of
modeling the distribution of New York City's taxi drop-off location given a
pick-up point. To our knowledge, our work is the first to propose such
conditional goodness-of-fit tests that simultaneously have all these desirable
properties.Comment: In UAI 2020. http://auai.org/uai2020/accepted.ph
Characterizations of non-normalized discrete probability distributions and their application in statistics
From the distributional characterizations that lie at the heart of Stein's
method we derive explicit formulae for the mass functions of discrete
probability laws that identify those distributions. These identities are
applied to develop tools for the solution of statistical problems. Our
characterizations, and hence the applications built on them, do not require any
knowledge about normalization constants of the probability laws. To demonstrate
that our statistical methods are sound, we provide comparative simulation
studies for the testing of fit to the Poisson distribution and for parameter
estimation of the negative binomial family when both parameters are unknown. We
also consider the problem of parameter estimation for discrete
exponential-polynomial models which generally are non-normalized.Comment: 24 pages, 3 figure