Model Criticism of Growth Curve Models via Posterior Predictive Model Checking

Model Checking Using Posterior Predictive Simulation

Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy

We propose a method to optimize the representation and distinguishability of samples from two probability distributions, by maximizing the estimated power of a statistical test based on the maximum mean discrepancy (MMD). This optimized MMD is applied to the setting of unsupervised learning by generative adversarial networks (GAN), in which a model attempts to generate realistic samples, and a ...

Bayesian Model Checking with Applications to Hierarchical Models 2 Full Prior Predictive Model Checking

In a Bayesian model with proper prior, all functions of the parameters and data are known. After observing the data, the joint prior speciication of data and parameters can be checked by comparing the posterior of any function of the parameters to its assumed prior. This paper gives checks for missing predictors, goodness-of-t, and over-diiuseness of the prior. The approach is illustrated in a ...

Posterior Predictive Assessment of Model Fitness via Realized Discrepancies

This paper considers Bayesian counterparts of the classical tests for goodness of fit and their use in judging the fit of a single Bayesian model to the observed data. We focus on posterior predictive assessment, in a framework that also includes conditioning on auxiliary statistics. The Bayesian formulation facilitates the construction and calculation of a meaningful reference distribution not...

Posterior Predictive Assessment of Model

This paper considers the Bayesian counterparts of the classical tests for goodness of t and their use in judging the t of a single Bayesian model to the observed data. We focus on posterior predictive assessment, in a framework that also includes conditioning on ancillary statistics. The Bayesian formulation facilitates the construction and calculation of a meaningful reference distribution not...