Bayesian Computations in Survival Models via the Gibbs Sampler

Abstract

Survival models used in biomedical and reliability contexts typically involve data censoring, and may also involve constraints in the form of ordered parameters. In addition, inferential interest often focuses on non-linear functions of natural model parameters. From a Bayesian statistical analysis perspective, these features combine to create difficult computational problems by seeming to require (multi-dimensional) numerical integrals over awkwardly defined regions. This paper illustrates how these apparent difficulties can be overcome, in both parametric and non-parametric settings, by the Gibbs sampler approach to Bayesian computation.

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Document Details

Document Type
Technical Report
Publication Date
Jul 01, 1991
Accession Number
ADA242343

Entities

People

  • Adrian F. Smith
  • Lynn Kuo

Organizations

  • Naval Postgraduate School

Tags

Communities of Interest

  • Biomedical

DTIC Thesaurus Topics

  • Artificial Intelligence
  • Bayesian Inference
  • Computational Science
  • Computations
  • Computer Science
  • Data Science
  • Estimators
  • Information Science
  • Monte Carlo Method
  • Numerical Integration
  • Operations Research
  • Probability
  • Random Variables
  • Sampling
  • Statistical Algorithms
  • Statistical Analysis
  • Statistics

Fields of Study

  • Computer science
  • Mathematics

Readers

  • Statistical inference.
  • Systems Analysis and Design

Technology Areas

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