Efficient Simulation from the Multivariate Normal and Student-t Distributions Subject to Linear Constraints,

Abstract

The construction and implementation of a Gibbs sampler for efficient simulation from the truncated multivariate normal and Student-t distributions is described. It is shown how the accuracy and convergence of integrals based on the Gibbs sample may be constructed. Bayesian inference; Gibbs sampler; Monte Carlo; Multiple integration, Truncated normal.

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1992
Accession Number
ADP007215

Entities

People

  • John Geweke

Organizations

  • University of Minnesota

Tags

DTIC Thesaurus Topics

  • Accuracy
  • Bayesian Inference
  • Computer Science
  • Construction
  • Convergence
  • Data Science
  • Engineering
  • Information Science
  • Integrals
  • Mathematics
  • Network Science
  • Simulations
  • Statistics
  • Theoretical Computer Science

Fields of Study

  • Mathematics

Readers

  • Statistical inference.

Technology Areas

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