Model comparisons and predictive mean computations for hierarchical Bayesian neural nets: quadratic approximation vs. MCMC

Author

Nakajima, Y. ; Asano, M. ; Nakada, Y. ; Matsumoto, T.

Author_Institution

Waseda Univ., Tokyo, Japan

Volume

1

fYear

1999

fDate

1999

Firstpage

137

Abstract

The article is a first step toward an attempt to demonstrate the validity of quadratic approximations (QAP) of computing marginal likelihood as well as predictive distributions for the hierarchical Bayesian scheme by using MCMC (Markov chains Monte Carlo). At least for the simple examples considered, the QAP gives reasonable results for marginal likelihood and predictive distributions. More elucidation is necessary to further study the issues for more complicated problems including nonlinear time series prediction problems

Keywords

Bayes methods; Markov processes; Monte Carlo methods; approximation theory; neural nets; MCMC; Markov chains Monte Carlo; QAP; hierarchical Bayesian neural nets; hierarchical Bayesian scheme; marginal likelihood; model comparisons; nonlinear time series prediction problems; predictive distributions; predictive mean computations; quadratic approximation; Annealing; Approximation algorithms; Bayesian methods; Context modeling; Distributed computing; Feedforward neural networks; Monte Carlo methods; Neural networks; Prediction algorithms; Predictive models;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Information Processing, 1999. Proceedings. ICONIP '99. 6th International Conference on

Conference_Location

Perth, WA

Print_ISBN

0-7803-5871-6

Type

conf

DOI

10.1109/ICONIP.1999.843975

Filename

843975