[KSCI] Korea Science Citation Index Service

http://dx.doi.org/10.5351/CKSS.2002.9.1.155

Bayesian Analysis for Neural Network Models

Chung, Younshik (Department of Statistics and Research Institute of Computer and Information Communication, Pusan National University)
Jung, Jinhyouk (DB Marketing Team, Sejung Co.)
Kim, Chansoo (Research Institute of Computer and Information Communication, Pusan National University)

Publication Information

Communications for Statistical Applications and Methods / v.9, no.1, 2002 , pp. 155-166 More about this Journal

Abstract

Neural networks have been studied as a popular tool for classification and they are very flexible. Also, they are used for many applications of pattern classification and pattern recognition. This paper focuses on Bayesian approach to feed-forward neural networks with single hidden layer of units with logistic activation. In this model, we are interested in deciding the number of nodes of neural network model with p input units, one hidden layer with m hidden nodes and one output unit in Bayesian setup for fixed m. Here, we use the latent variable into the prior of the coefficient regression, and we introduce the 'sequential step' which is based on the idea of the data augmentation by Tanner and Wong(1787). The MCMC method(Gibbs sampler and Metropolish algorithm) can be used to overcome the complicated Bayesian computation. Finally, a proposed method is applied to a simulated data.

Keywords

Neural network; Latent variab1e; Sequential step; Gibbs sampler; Metropolish algorithm; Transfer function;

Citations & Related Records

Times Cited By KSCI : 2 (Citation Analysis)

Reference
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