Academic
Publications
BAYESIAN MODEL AVERAGING AND BAYESIAN PREDICTIVE INFORMATION CRITERION FOR MODEL SELECTION

BAYESIAN MODEL AVERAGING AND BAYESIAN PREDICTIVE INFORMATION CRITERION FOR MODEL SELECTION,Tomohiro Ando

BAYESIAN MODEL AVERAGING AND BAYESIAN PREDICTIVE INFORMATION CRITERION FOR MODEL SELECTION   (Citations: 3)
BibTex | RIS | RefWorks Download
The problem of evaluating the goodness of the predictive distributions devel- oped by the Bayesian model averaging approach is investigated. Considering the maximization of the posterior mean of the expected log-likelihood of the predictive distributions (Ando (2007a)), we develop the Bayesian predictive information crite- rion (BPIC). According to the numerical examples, we show that the posterior mean of the log-likelihood has a positive bias comparing with the posterior mean of the expected log-likelihood, and that the bias estimate of BPIC is close to the true bias. One of the advantages of BPIC is that we can optimize the size of Occam's razor. Monte Carlo simulation results show that the proposed method performs well.
Published in 2008.
Cumulative Annual
View Publication
The following links allow you to view full publications. These links are maintained by other sources not affiliated with Microsoft Academic Search.
Sort by: