## parameters such as cr and A, that is Pr(lw , or) = Pr(). Posterior probabilities of network weights are as follows. For regression with Gaussian error and unknown a,

by
Pr Pr Pt

### BibTeX

@MISC{Pt_parameterssuch,

author = {Pr Pr Pt},

title = {parameters such as cr and A, that is Pr(lw , or) = Pr(). Posterior probabilities of network weights are as follows. For regression with Gaussian error and unknown a,},

year = {}

}

### OpenURL

### Abstract

This paper has covered Bayesian theory relevant to the problem of training feed-forward connectionist networks. We now sketch out how this might be put together in practice, assuming a standard gradient descent algorithm as used during search