The following preprint is now available by anonymous ftp.

[David J.C. MacKay]

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	Bayesian Neural Networks and Density Networks

              David J.C. MacKay

                      University of Cambridge
                      Cavendish Laboratory
                      Madingley Road
                      Cambridge CB3 0HE
                      mackay at mrao.cam.ac.uk

	This paper reviews the Bayesian approach to learning in neural
	networks, then introduces a new adaptive model, the density
	network. This is a neural network for which target outputs are
	provided, but the inputs are unspecified. When a probability
	distribution is placed on the unknown inputs, a latent
	variable model is defined that is capable of discovering the
	underlying dimensionality of a data set.  A Bayesian learning
	algorithm for these networks is derived and demonstrated 
	with an application to the modelling of protein families.

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The preprint may be obtained as follows: 

ftp 131.111.48.8
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cd pub/mackay/density
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mget *.ps.Z
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uncompress *.ps.Z

This document is 12 pages long. 
Sorry, hard copy is not available from the author.