Bayesian Model Choice: Asymptotics and Exact Calculations

Abstract

Model determination is a fundamental data analytic task. Here we consider the problem of choosing amongst a finite (with loss of generality we assume two) set of models. After briefly reviewing classical and Bayesian model choice strategies we present a general predictive density which includes all proposed Bayesian approaches we are aware of. Using Laplace approximations we can conveniently assess and compare asymptotic behavior of these approaches. Concern regarding the accuracy of these approximation for small to moderate sample sizes encourages the use of Monte Carlo techniques to carry out exact calculations. A data set fit with nested non linear models enables comparison between proposals and between exact and asymptotic values.

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Document Details

Document Type
Technical Report
Publication Date
Jun 15, 1993
Accession Number
ADA269067

Entities

People

  • Alan E. Gelfand
  • D. K. Dey

Organizations

  • Stanford University

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Accuracy
  • Bayesian Inference
  • Bayesian Networks
  • Computational Science
  • Data Science
  • Data Sets
  • Estimators
  • Information Processing
  • Information Science
  • Markov Chains
  • Monte Carlo Method
  • Probability
  • Sampling
  • Simulations
  • Statistical Algorithms
  • Statistics
  • United States

Fields of Study

  • Mathematics

Readers

  • Computational Modeling and Simulation
  • Mathematical Modeling and Probability Theory.
  • Theoretical Analysis.

Technology Areas

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