Xref: utzoo comp.ai:2093 comp.ai.neural-nets:179
Path: utzoo!attcan!uunet!husc6!bloom-beacon!oberon!brand.usc.edu!manj
From: manj@brand.usc.edu (B. S. Manjunath)
Newsgroups: comp.ai,comp.ai.neural-nets
Subject: Re: refs. for stochastic relaxation
Message-ID: <11408@oberon.USC.EDU>
Date: 6 Aug 88 18:02:44 GMT
References: <824@uhnix1.uh.edu>
Sender: news@oberon.USC.EDU
Reply-To: manj@brand.usc.edu (B. S. Manjunath)
Organization: University of Southern California, Los Angeles, CA
Lines: 24

In article <824@uhnix1.uh.edu> rmdubash@sun2.cs.uh.edu () writes:
>I am currently working on stochastic relaxation and relaxation algorithms for 
>finely grained  parallel  architectures.  In particular, I am  studying their 
>implementation on neural and connectionist models, with emphasis on  inherent
>fault tolerance property of such implementations.
>
>I will be grateful if any of you can provide me with pointers, references etc.
>on this ( or related ) topics.

>Rumi Dubash, Computer Science, Univ. of Houston,

 Geman and Geman (1984) is an excellent paper to start with. It also 
contains lot of refernces. The paper mainly deals with Markov Random Fields 
and applications to image processing. 

S.Geman and D.Geman,"Stochastic relaxation, Gibbs distributions and 
the bayesian restoration of images", IEEE trans. on pattern analysis 
and machine intelligence", PAMI-6,Nov 1984, pp. 721-742.

Another reference that I feel might be useful is Marroquin,J.L.
Ph. D Thesis "Probabilistic solution of Inverse problems",
M.I.T. 1985.

bs manjunath.