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

### From inside the book

Results 1-3 of 35

Page 202

When such knowledge is available, it must be incorporated into the

fact, most of the commonly used life distributions have a mouotonically increasing

failure ...

When such knowledge is available, it must be incorporated into the

**prior****distribution**. In reliability theory, monotone ... rate functions play a central role. Infact, most of the commonly used life distributions have a mouotonically increasing

failure ...

Page 203

These restrictions must be reflected in the

to represent informed opinion. This may be accomplished by using a Dirichlet

u3 ...

These restrictions must be reflected in the

**prior**joint**distribution**of the «, in orderto represent informed opinion. This may be accomplished by using a Dirichlet

**distribution**as the**prior**joint**distribution**for the random variables Mi — u.,, m2 —u3 ...

Page 385

Bayesian inference for the unknown parameters N and A involves assigning a

distribution using Tlt Tk and the Bayes theorem. Our uncertainty in knowledge

about N ...

Bayesian inference for the unknown parameters N and A involves assigning a

**prior distribution**to the pair (N, A), and then obtaining the resultant posteriordistribution using Tlt Tk and the Bayes theorem. Our uncertainty in knowledge

about N ...

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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