Random Parameter Markov Population Process Models and Their Likelihood, Bayes and Empirical Bayes Analysis.

Abstract

Markov population stochastic processes are useful in describing repairman and logistics problems, networks of queues, pharmacological processes, and manpower situations. This paper considers statistical estimation problems arising for such mathematical models. Parameter estimation of an empirical Bayes nature, with limited shrinkage or discrepancy tolerant features is discussed and illustrated. Additional keywords: Maximum likelihood estimation; Pharmacology; Statistical inference; Statistical analysis. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 1985
Accession Number
ADA159812

Entities

People

  • Donald P. Gaver
  • J. P. Lehoczky

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Biomedical
  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Bayesian Inference
  • Data Science
  • Equations
  • Estimators
  • Information Science
  • Mathematical Models
  • Mathematics
  • Military Research
  • Operations Research
  • Probability
  • Public Health
  • Random Variables
  • Statistical Algorithms
  • Statistical Analysis
  • Statistical Inference
  • Statistics
  • Stochastic Processes

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Mathematical Modeling and Probability Theory.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference