maureen@ai.toronto.edu (06/24/91)
Archive-name: ai/neuroprose/neal-bayes/1991-06-20
Archive: cheops.cis.ohio-state.edu:/pub/neuroprose/neal.bayes.ps.Z [128.146.8.62]
Original-posting-by: maureen@ai.toronto.edu
Original-subject: [Maureen Smith: Announce new CRG Technical Report]
Reposted-by: emv@msen.com (Edward Vielmetti, MSEN)
------- Forwarded Message
From: Maureen Smith <maureen@ai.toronto.edu>
Subject: Announce new CRG Technical Report
Message-Id: <91Jun19.113852edt.780@neuron.ai.toronto.edu>
Date: Wed, 19 Jun 1991 11:38:49 -0400
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
------- End of Forwarded Message
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