Hierarchical Bayesian Analysis of Change Point Problems

Abstract

A general approach to hierarchical Bayes change point models is presented. In particular desired marginal posterior densities are obtained utilizing the Gibbs sampler, an iterative Monte Carlo method. This approach avoids sophisticated analytic and numerical high dimensional integration procedures. We include application to changing regressions, changing Poisson processes, and changing Markov chains. Within these contexts we handle several previously inaccessible problems. (kr)

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Document Details

Document Type
Technical Report
Publication Date
Oct 18, 1990
Accession Number
ADA228179

Entities

People

  • Adrian F. Smith
  • Alan E. Gelfand
  • Bradley P. Carlin

Organizations

  • Stanford University

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Bayesian Inference
  • Bayesian Networks
  • Computational Science
  • Data Analysis
  • Data Science
  • Data Sets
  • Information Science
  • Markov Chains
  • Monte Carlo Method
  • Probability
  • Random Variables
  • Sampling
  • Statistical Algorithms
  • Statistical Analysis
  • Statistics
  • Surveys

Fields of Study

  • Mathematics

Readers

  • Agent-Based Social Robotics and Mobile-Assisted Learning in Virtual Environments.
  • Statistical inference.

Technology Areas

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