## Proceedings of the International School of Physics "Enrico Fermi", Volume 94 |

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Page 204

3 ) Values for the prior

subjectively pleasing manner . 32 . Interpretation of prior

keeping with a complete Bayesian analysis the user should be able to , a priori ,

arrive at a ...

3 ) Values for the prior

**parameters**may be obtained in a straightforward ,subjectively pleasing manner . 32 . Interpretation of prior

**parameters**. – Inkeeping with a complete Bayesian analysis the user should be able to , a priori ,

arrive at a ...

Page 385

4 ) and treat A and N as the unknown

unknown

N , 1 ) , and then obtaining the resultant posterior distribution using T2 , . . . , Tx

and ...

4 ) and treat A and N as the unknown

**parameters**. Bayesian inference for theunknown

**parameters**N and A involves assigning a prior distribution to the pair (N , 1 ) , and then obtaining the resultant posterior distribution using T2 , . . . , Tx

and ...

Page 427

The objective of this lecture is to present a general framework for constructing

and estimating the

testing protocol used , the

very ...

The objective of this lecture is to present a general framework for constructing

and estimating the

**parameters**of ... the type of model selected as well as thetesting protocol used , the

**parameter**estimation problems are mathematicallyvery ...

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### Contents

STATISTICAL THEORY OF RELIABLITY | 8 |

Definitions and characterizations | 12 |

J KEILSON Stochastic models in reliability theory | 23 |

Copyright | |

37 other sections not shown

### Common terms and phrases

analysis application approach associated assume assumption BARLOW Bayesian calculation called complex components consider constant continuous correctness Course defined density depends derived described detected determine discussed distribution edited epochs equations equivalence ergodic errors estimate example exists expected exponential fact fail failure rate fault function given Hence important increasing independent input integration interest interval known likelihood limit Markov matrix mean measure method modules normal Note observed obtain occur operational parameters performance phase positive possible posterior predictive prior probability problem procedure prove random variables renewal repair requirements rule sample selected sequence simple software reliability space specification statistical stochastic structure Suppose task theorem theory tion transition tree University values York