Sampling Based Approach to Computing Nonparametric Bayesian Estimators with Doubly Censored Data,

Abstract

Nonparametric Bayesian estimators with Dirichlet process priors for doubly censored data can be derived from mixtures of Dirichlet distributions. To circumvent the computational difficulties in evaluating these mixtures, this paper describes the Gibbs sampling approach to approximating them. The Gibbs samplers augment the censored data by the number of observations falling into each interval. An example taken from Turnbull (1974) is given to illustrate the roach. Gibbs sampling; Stochastic substitution; Dirichlet process priors; Doubly censored data.

Document Details

Document Type
Technical Report
Publication Date
Jan 01, 1992
Accession Number
ADP007223

Entities

People

  • Lynn Kuo

Organizations

  • University of Connecticut

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Algorithms
  • Collecting Methods
  • Computer Science
  • Data Science
  • Engineering
  • Estimators
  • Information Science
  • Intervals
  • Mathematics
  • Monte Carlo Method
  • Network Science
  • Observation
  • Sampling
  • Statistical Algorithms
  • Statistics
  • Theoretical Computer Science

Fields of Study

  • Mathematics

Readers

  • Statistical inference.
  • Systems Analysis and Design

Technology Areas

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