Conference on Stochastic Processes and Their Applications (12th) held at Ithaca, New York on 11-15 Jul 83,

Abstract

Contents: Measures with given marginals; Reflecting Brownian motion; Extensions and invariant measures; Dyson's hierarchial model; One dimensional stochastic Ising models; Markov decision processes; Prophet problems; Numerical methods; Stationary queues; Ergodicity and percolation; Thinning of point processes; What is a stable population? Quantum diffusion; Schrodinger equations; Random fractiles; Representation of Hunt processes; Fiber-matrix composite materials; Random fields; Branching processes; Time series; Stochastic models 1; Stable processes; Reliability; Stochastic control and optimal stopping 1; Markov processes, Statistical inference from stochastic processes; Stochastic Integration; Self similar proceses; Queueing theory; Stochastic control and optimal stopping 2; Markov and renewal processes; Random fields; Renewal theory and random walks; Stochastic models 2; Characterization and limit theorems; Mixing conditions and limit theorems; Diffusion processes; and Stochastic models 3.

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Document Details

Document Type
Technical Report
Publication Date
Jul 15, 1983
Accession Number
ADA136626

Entities

People

  • D. C. Heath
  • E. B. Dynkin
  • F. L. Spitzer
  • H. Kesten
  • M. S. Taqqu

Organizations

  • Cornell University

Tags

Communities of Interest

  • Biomedical
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Applied Mathematics
  • Banach Space
  • Chemical Reactions
  • Computational Science
  • Data Science
  • Difference Equations
  • Differential Equations
  • Information Science
  • Military Research
  • Operations Research
  • Queueing Theory
  • Random Variables
  • Statistical Inference
  • Stochastic Control
  • Stochastic Processes
  • Surveys
  • Theorems

Fields of Study

  • Mathematics

Readers

  • Mathematical Modeling and Probability Theory.
  • Statistical inference.

Technology Areas

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