Announce new CRG Technical Report

[Maureen Smith]

The following technical report is available for ftp from the neuroprose
archive. A hardcopy may also be requested. (See below for details.)

Though written for a statistics audience, this report should be of interest
to connectionists and others interested in machine learning, as it reports
a Bayesian solution for one type of "unsupervised concept learning". The
technique employed is also related to that used in Boltzmann Machines.


            Bayesian Mixture Modeling by Monte Carlo Simulation

                             Radford M. Neal

                       Technical Report CRG-TR-91-2
                      Department of Computer Science
                          University of Toronto

    It is shown that Bayesian inference from data modeled by a mixture
    distribution can feasibly be performed via Monte Carlo simulation.
    This method exhibits the true Bayesian predictive distribution,
    implicitly integrating over the entire underlying parameter space. 
    An infinite number of mixture components can be accommodated without
    difficulty, using a prior distribution for mixing proportions that
    selects a reasonable subset of components to explain any finite
    training set. The need to decide on a ``correct'' number of components
    is thereby avoided. The feasibility of the method is shown empirically
    for a simple classification task.


To obtain a compressed PostScript version of this report from neuroprose,
ftp to "cheops.cis.ohio-state.edu" (128.146.8.62), log in as "anonymous"
with password "neuron", set the transfer mode to "binary", change to the
directory "pub/neuroprose", and get the file "neal.bayes.ps.Z". Then
use the command "uncompress neal.bayes.ps.Z" to convert the file to 
PostScript.

To obtain a hardcopy version of the paper by physical mail, send mail
to :		Maureen Smith 
		Department of Computer Science
		University of Toronto
		6 King's College Road
		Toronto, Ontario
		M5A 1A4