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

### From inside the book

Results 1-3 of 84

Page 156

Then , conditional on Y < t , and to , the density of Y is

exp [ - 2to ] ) for 0 < y < to . Thus , if the successive failure ages actually observed

are denoted by Yı , Y2 , . . . , Yx , the corresponding conditional joint density is ...

Then , conditional on Y < t , and to , the density of Y is

**given**by a exp [ - 2y ] / ( 1 –exp [ - 2to ] ) for 0 < y < to . Thus , if the successive failure ages actually observed

are denoted by Yı , Y2 , . . . , Yx , the corresponding conditional joint density is ...

Page 206

Under the DFR assumption the posterior distribution of the u ; is

fo ( us , r ) C ( 1 – ur . ) ? – U ; ) " up ! ( U ; — U ; - ) " 81 – 1 . 1 - 1 In order to

completely specify these distributions , the constant of integration must be

obtained .

Under the DFR assumption the posterior distribution of the u ; is

**given**by ( 4 . 4 )fo ( us , r ) C ( 1 – ur . ) ? – U ; ) " up ! ( U ; — U ; - ) " 81 – 1 . 1 - 1 In order to

completely specify these distributions , the constant of integration must be

obtained .

Page 218

For instance , we may consider the history generalized by a

denoted by F * = o { X , 8 < t } . We have to be sure which history we refer to , and

explicitly mention it if it is not obvious ; for instance ,

For instance , we may consider the history generalized by a

**given**process X ,denoted by F * = o { X , 8 < t } . We have to be sure which history we refer to , and

explicitly mention it if it is not obvious ; for instance ,

**given**a parallel of two items ...### What people are saying - Write a review

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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