Markov Chain Monte Carlo Maximum Likelihood,

Abstract

Markov chain Monte Carlo (e. g., the Metropolis algorithm and Gibbs sampler) is a general tool for simulation of complex stochastic processes useful in many types of statistical inference. The basics of Markov chain Monte Carlo are reviewed, including choice of algorithms and variance estimation, and some new methods are introduced. The use of Markov chain Monte Carlo for maximum likelihood estimation is explained, and its performance is compared with maximum pseudo likelihood estimation. Markov chain, Monte Carlo, Maximum likelihood, Metropolis algorithm, Gibbs sampler, Variance estimation.

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1992
Accession Number
ADP007125

Entities

People

  • Charles J. Geyer

Organizations

  • University of Minnesota

Tags

DTIC Thesaurus Topics

  • Algorithms
  • Computer Science
  • Data Science
  • Information Science
  • Markov Chains
  • Maximum Likelihood Estimation
  • Monte Carlo Method
  • Network Science
  • Statistical Algorithms
  • Statistical Inference
  • Statistics
  • Stochastic Processes
  • Theoretical Computer Science

Fields of Study

  • Mathematics

Readers

  • Geotechnical Engineering.
  • Mathematical Modeling and Probability Theory.
  • Regression Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference