Stochastically-Ordered Parameters in Bayesian Prediction.

Abstract

In models of reliability growth in stages, it is usual to assume that system parameters improve monotonically from stage to stage, following some postulated law of growth. This paper explores a Bayesian model where such improvement only occurs on the average, e.g., a case when the parameters are assumed to be stochastically ordered. It is shown that the problem can be recast into a hierarchical form in which there are strictly-ordered hyperparameters which index the admissible family of ordered distributions for the parameters; the modelling problem is then to describe an appropriate law of motion over the hyperparameters. (Author)

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1979
Accession Number
ADA100419

Entities

People

  • William S. Jewell

Organizations

  • University of California, Berkeley

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Fields of Study

  • Mathematics

Readers

  • Statistical inference.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference
  • AI & ML - Information Retrieval
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks