Bayesian Analysis of Linear and Nonlinear Population Models Using the Gibbs Sampler

Abstract

A fully Bayesian analysis of linear and nonlinear population models has previously been unavailable, as a consequence of the seeming impossibility of performing the necessary numerical Integrations in the complex multi- parameter structures typically arising in such models. It is demonstrated that, for a variety of linear and nonlinear population models, a fully Bayesian analysis can be implemented in a straightforward manner using the Gibbs sampler. The approach is illustrated with examples involving challenging problems of outliers and mean-variance relationships in population modelling.

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Document Details

Document Type
Technical Report
Publication Date
Jul 21, 1992
Accession Number
ADA254769

Entities

People

  • A. E. Gelfand
  • A. F. Smith
  • A. Racine-poon
  • J. C. Wakefield

Organizations

  • Stanford University

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Artificial Intelligence
  • Bayesian Inference
  • Bayesian Networks
  • Computational Science
  • Data Science
  • Demography
  • Information Science
  • Monte Carlo Method
  • New York
  • Nonlinear Dynamics
  • Normal Distribution
  • Probability
  • Random Variables
  • Sampling
  • Statistical Algorithms
  • Statistics

Fields of Study

  • Mathematics

Readers

  • Computational Fluid Dynamics (CFD)
  • Materials Science and Engineering.
  • Regression Analysis.

Technology Areas

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