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Continuous Stochastic Cellular Automata that Have a Stationary Distribution and No Detailed Balance

Marroquin and Ramirez (1990) have recently discovered a class of discrete stochastic cellular automata with Gibbsian invariant measures that have a non-reversible dynamic behavior. Practical applications include more powerful algorithms than the Metropolis algorithm to compute MRF models. In this paper we describe a large class of stochastic dynamical systems that has a Gibbs asymptotic distribution but does not satisfy reversibility. We characterize sufficient properties of a sub-class of stochastic differential equations in terms of the associated Fokker-Planck equation for the existence of an asymptotic probability distribution in the system of coordinates which is given. Practical implications include VLSI analog circuits to compute coupled MRF models.

Identiferoai:union.ndltd.org:MIT/oai:dspace.mit.edu:1721.1/6012
Date01 December 1990
CreatorsPoggio, Tomaso, Girosi, Federico
Source SetsM.I.T. Theses and Dissertation
Languageen_US
Detected LanguageEnglish
Format6 p., 35936 bytes, 134518 bytes, application/octet-stream, application/pdf
RelationAIM-1168

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