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

### From inside the book

Results 1-3 of 72

Page 208

In actuality , the KF may be easily understood by the statistician if it is cast as a

problem in Bayesian inference and use is made of some well -

multivariate statistics . This feature was evidently first published by HARRISON

and ...

In actuality , the KF may be easily understood by the statistician if it is cast as a

problem in Bayesian inference and use is made of some well -

**known**results inmultivariate statistics . This feature was evidently first published by HARRISON

and ...

Page 388

The prior distribution for N is Poisson with a

parameter 1 is degenerate at a

assumptions describe case 1 of subsect . 3 ' 1 . Assumption A3 is , from a

subjective ...

The prior distribution for N is Poisson with a

**known**parameter 0 . A5 . Theparameter 1 is degenerate at a

**known**a ; that is , P { 1 = 1 } = 1 . Note that theseassumptions describe case 1 of subsect . 3 ' 1 . Assumption A3 is , from a

subjective ...

Page 475

... training deficiency inexperienced operator definition / selection procedure not

followed premature commitment to strategy plant response , characteristics not

completely

of ...

... training deficiency inexperienced operator definition / selection procedure not

followed premature commitment to strategy plant response , characteristics not

completely

**known**pending activity competes for attention premature terminationof ...

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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