DTIC ADA1004197: Stochastically Ordered Parameters in Bayesian pdf

DTIC ADA1004197: Stochastically Ordered Parameters in Bayesian

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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

• Creator/s: Defense Technical Information Center
• Date: 10/1/1979
• Year: 1979
• Book Topics/Themes: DTIC Archive, Jewell, William S, CALIFORNIA UNIV BERKELEY OPERATIONS RESEARCH CENTER, *STOCHASTIC PROCESSES, *PARAMETERS, *MATHEMATICAL PREDICTION, *BAYES THEOREM, MATHEMATICAL MODELS, RANDOM VARIABLES, RELIABILITY, EXPONENTIAL FUNCTIONS, MARKOV PROCESSES

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