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 -- MSEN Archive Service file verification cheops.cis.ohio-state.edu -rw-r--r-- 1 3169 274 122045 Jun 12 17:58 /pub/neuroprose/neal.bayes.ps.Z found neal-bayes ok cheops.cis.ohio-state.edu:/pub/neuroprose/neal.bayes.ps.Z