NDLI: Approximating cross-validatory predictive evaluation in Bayesian latent variable models with integrated IS and WAIC

Content Provider	Springer Nature Link
Author	Li, Longhai Qiu, Shi Zhang, Bei Feng, Cindy X.
Copyright Year	2015
Abstract	Looking at predictive accuracy is a traditional method for comparing models. A natural method for approximating out-of-sample predictive accuracy is leave-one-out cross-validation (LOOCV)—we alternately hold out each case from a full dataset and then train a Bayesian model using Markov chain Monte Carlo without the held-out case; at last we evaluate the posterior predictive distribution of all cases with their actual observations. However, actual LOOCV is time-consuming. This paper introduces two methods, namely iIS and iWAIC, for approximating LOOCV with only Markov chain samples simulated from a posterior based on a full dataset. iIS and iWAIC aim at improving the approximations given by importance sampling (IS) and WAIC in Bayesian models with possibly correlated latent variables. In iIS and iWAIC, we first integrate the predictive density over the distribution of the latent variables associated with the held-out without reference to its observation, then apply IS and WAIC approximations to the integrated predictive density. We compare iIS and iWAIC with other approximation methods in three kinds of models: finite mixture models, models with correlated spatial effects, and a random effect logistic regression model. Our empirical results show that iIS and iWAIC give substantially better approximates than non-integrated IS and WAIC and other methods.
Starting Page	881
Ending Page	897
Page Count	17
File Format	PDF
ISSN	09603174
Journal	Statistics and Computing
Volume Number	26
Issue Number	4
e-ISSN	15731375
Language	English
Publisher	Springer US
Publisher Date	2015-06-14
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	MCMC Cross-validation Posterior predictive check Predictive model assessment DIC WAIC Bayesian latent variable models Statistics and Computing/Statistics Programs Artificial Intelligence (incl. Robotics) Statistical Theory and Methods Probability and Statistics in Computer Science
Content Type	Text
Resource Type	Article
Subject	Statistics and Probability Theoretical Computer Science Computational Theory and Mathematics Statistics, Probability and Uncertainty

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