Journal
COMPUTATIONAL STATISTICS & DATA ANALYSIS
Volume 79, Issue -, Pages 277-291Publisher
ELSEVIER
DOI: 10.1016/j.csda.2014.05.012
Keywords
Bayesian model checking; Posterior predictive p value; Sampled posterior p value; Calibrated posterior predictive p value; Hierarchical model; Causal effect
Funding
- Key Laboratory of Mathematical Economics and Quantitative Finance (Peking University), Ministry of Education, China
Ask authors/readers for more resources
Bayesian p values are a popular and important class of approaches for Bayesian model Checking. They are used to quantify the degree of surprise from the observed data given the specified data model and prior distribution. A systematic investigation is conducted to compare three Bayesian p values - the posterior predictive p value, the sampled posterior p value and the calibrated posterior predictive p value. Their general computation costs are compared, and several examples that incorporate both simple and complex Bayesian models are used to compare their frequency properties. It is recommended to use the sampled posterior p value because it is computationally least expensive and safest. (C) 2014 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available