Risk-Sensitive Probability for Markov Chains

Abstract

The probability distribution of a Markov chain is viewed as the information state of an additive optimization problem. This optimization problem is then generalized to a product form whose information state gives rise to a generalized notion of probability distribution for Markov chains. The evolution and the asymptotic behavior of this generalized or "risk-sensitive" probability distribution is studied in this paper and a conjecture is proposed regarding the asymptotic periodicity of risk-sensitive probability. The relation between a set of simultaneous nonlinear equations and the set of periodic attractors is analyzed.

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Document Details

Document Type
Technical Report
Publication Date
Sep 17, 2002
Accession Number
ADA438509

Entities

People

  • Steven I Marcus
  • Vahid R. Ramezani

Organizations

  • University of Maryland

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Computational Science
  • Cross Flow
  • Equations
  • Estimators
  • Hidden Markov Models
  • Markov Chains
  • Markov Models
  • Mixing
  • Observation
  • Periodic Variations
  • Probabilistic Models
  • Probability
  • Probability Distributions
  • Quadratic Equations
  • Random Variables
  • Simulations
  • Stationary

Fields of Study

  • Mathematics

Readers

  • Geospatial Intelligence and Artificial Intelligence Analytics
  • Graph Algorithms and Convex Optimization.