Likelihood and the Bayes procedure

1998 ◽  
pp. 309-332 ◽  
Author(s):  
Hirotugu Akaike
Keyword(s):  
Bayes Procedure

scholarly journals An iterative Bayes procedure for reliability assessment

2002 ◽  
Author(s):  
R.R. Prairie ◽  
W.J. Zimmer
Keyword(s):  
Reliability Assessment ◽  
Bayes Procedure

scholarly journals Computation of posterior distribution in Bayesian analysis – application in an intermittently used reliability system

2002 ◽  
Vol 21 (3) ◽  
pp. 78-82
Author(s):  
V. S.S. Yadavalli ◽  
P. J. Mostert ◽  
A. Bekker ◽  
M. Botha
Keyword(s):  
Posterior Distribution ◽  
Jeffreys Prior ◽  
Posterior Density ◽  
Unknown Parameters ◽  
Stationary Rate ◽  
Analysis Application ◽  
Definition Of ◽  
Hpd Intervals ◽  
Highest Posterior Density ◽  
Bayes Procedure

Bayesian estimation is presented for the stationary rate of disappointments, D∞, for two models (with different specifications) of intermittently used systems. The random variables in the system are considered to be independently exponentially distributed. Jeffreys’ prior is assumed for the unknown parameters in the system. Inference about D∞ is being restrained in both models by the complex and non-linear definition of D∞. Monte Carlo simulation is used to derive the posterior distribution of D∞ and subsequently the highest posterior density (HPD) intervals. A numerical example where Bayes estimates and the HPD intervals are determined illustrates these results. This illustration is extended to determine the frequentistical properties of this Bayes procedure, by calculating covering proportions for each of these HPD intervals, assuming fixed values for the parameters.

scholarly journals AN EMPIRICAL BAYES PROCEDURE FOR FINDING AN INTERVAL ESTIMATE

1971 ◽  
Vol 1971 (2) ◽  
pp. i-20
Author(s):  
Frederic M. Lord ◽  
Noel Cressie
Keyword(s):  
Empirical Bayes ◽  
Interval Estimate ◽  
Bayes Procedure

An empirical Bayes procedure for adaptive forecasting of shrimp yield

Aquaculture ◽  
2000 ◽  
Vol 182 (3-4) ◽  
pp. 215-228 ◽  
Author(s):  
David G. Whiting ◽  
H.Dennis Tolley ◽  
Gilbert W. Fellingham
Keyword(s):  
Empirical Bayes ◽  
Bayes Procedure

Bayes Inference for Repairable Mechanical Units with Imperfect or Hazardous Maintenance

1998 ◽  
Vol 05 (01) ◽  
pp. 65-83 ◽  
Author(s):  
Raffaela Calabria ◽  
Gianpaolo Pulcini
Keyword(s):  
Power Law ◽  
Prior Information ◽  
Repair Process ◽  
Suitable Model ◽  
Bayes Inference ◽  
Failure Times ◽  
Failure Data ◽  
Failure Intensity ◽  
Future Sample ◽  
Bayes Procedure

The Modulated Power Law process (MPLP) has been recently proposed as a suitable model to describe the failure pattern of repairable mechanical units subject to imperfect or hazardous maintenance. In this paper, an informative Bayes procedure is proposed to analyze failure data arising from a MPLP sample, which allows prior information on the failure/repair process to be incorporated into the inferential procedure. Inference both of the MPLP parameters and of some functions thereof (such as the unconditional mean number of failures and the unconditional failure intensity), as well as prediction on failure times in a future sample, is developed. Finally, a numerical example is given to illustrate the proposed procedure.

On the empirical bayes procedure

1968 ◽  
Vol 20 (1) ◽  
pp. 169-185 ◽  
Author(s):  
Hirosi Hudimoto
Keyword(s):  
Empirical Bayes ◽  
Bayes Procedure

On Selection Procedures Based on The Exceedance Probability

1976 ◽  
Vol 25 (1-4) ◽  
pp. 29-40 ◽  
Author(s):  
Umesh D. Naik
Keyword(s):  
The Other ◽  
Sequential Procedure ◽  
Exceedance Probability ◽  
Selection Procedures ◽  
Normal Populations ◽  
Two Populations ◽  
Bayes Procedure ◽  
Selection Of ◽  
Better Than

A number of normal populations are to be compared in terms of exceedance probability. When comparing two populations, a population is to be designated as better than the other if it has a greater exceedance probability. A Bayes procedure is given for selecting the subset of populations which contains the best population. A Bayes sequential procedure for selection of the best population is also described.

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