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

Abstract

Marroquin and Ramirez (1990) have recently discovered a class of discrete stochastic cellular automata with Gibbsian invariant measure 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 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.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1990
Accession Number
ADA234421

Entities

People

  • Federico Girosi
  • Tomaso Poggio

Organizations

  • Massachusetts Institute of Technology

Tags

Communities of Interest

  • Air Platforms

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Automata
  • Cognitive Science
  • Department Of Defense
  • Differential Equations
  • Equations
  • Fokker Planck Equations
  • Gaussian Noise
  • Information Processing
  • Information Systems
  • Mathematical Analysis
  • Military Research
  • Partial Differential Equations
  • Probability
  • Probability Distributions
  • Stationary

Fields of Study

  • Mathematics

Readers

  • Mathematical Modeling and Probability Theory.
  • Statistical inference.