Large Deviations for Stochastic Flows of Diffeomorphisms

Abstract

A large deviation principle is established for a general class of stochastic flows in the small noise limit. This result is then applied to a Bayesian formulation of an image matching problem, and an approximate maximum likelihood property is shown for the solution of an optimization problem involving the large deviations rate function.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2007
Accession Number
ADA476150

Entities

People

  • Amarjit Budhiraja
  • Paul Dupuis
  • Vasileios Maroulas

Organizations

  • University of North Carolina at Chapel Hill

Tags

Communities of Interest

  • Biomedical
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Applied Mathematics
  • Bayesian Networks
  • Brownian Motion
  • Convergence
  • Differential Equations
  • Equations
  • Hilbert Space
  • Inequalities
  • Military Research
  • Operations Research
  • Probability
  • Probability Distributions
  • Random Variables
  • Statistical Distributions
  • Stochastic Processes
  • Topology
  • Weak Convergence

Fields of Study

  • Mathematics

Readers

  • Operations Research
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Machine Learning Algorithms