Bayesian Nonparametric Statistical Inference for Shock Models and Wear Processes.

Abstract

Statistical procedures for shock models and wear processes are considered in this paper. We show that independent gamma-Dirichlet priors are conjugate priors when sampling from these shock models. Bayes rules given the observations are computed. In particular, we calculate the Bayes estimates of the survival probabilities for these models. We show consistency of the posterior distribution as well as weak convergence of the centered and suitably rescaled posterior processes. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1979
Accession Number
ADA083175

Entities

People

  • Albert Y. Lo

Organizations

  • University of California, Berkeley

Tags

Communities of Interest

  • Materials and Manufacturing Processes

DTIC Thesaurus Topics

  • Abstracts
  • California
  • Consistency
  • Convergence
  • Data Science
  • Distribution Functions
  • Information Science
  • New York
  • Observation
  • Operations Research
  • Probability
  • Random Variables
  • Statistical Inference
  • Statistics
  • Survival
  • United States
  • Weak Convergence

Fields of Study

  • Mathematics

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Mathematical Modeling and Probability Theory.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference