Convergence of Quasi-Stationary to Stationary Distributions for Stochastically Monotone Markov Processes.

Abstract

It is shown that if a stochastically monotone Markov process of (0, infinity) with stationary distribution H has its state space truncated by making all states in (B, infinity) absorbing, then the quasi-stationary distribution of the new process coverages to H as B approaches limit of infinity. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1984
Accession Number
ADA148041

Entities

People

  • David Siegmund
  • Martin R. Pollak

Organizations

  • Stanford University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Brownian Motion
  • Continuity
  • Convergence
  • Diffusion
  • Markov Chains
  • Markov Processes
  • Military Research
  • New York
  • Probability
  • Probability Distributions
  • Stationary
  • United States
  • United States Government
  • Universities

Fields of Study

  • Mathematics

Readers

  • Computational Modeling and Simulation
  • Fluid Dynamics.
  • Graph Algorithms and Convex Optimization.

Technology Areas

  • Space
  • Space - Spacecraft Maneuvers