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

### From inside the book

Results 1-3 of 50

Page 32

At each epoch the system regenerates in that its statistical future depends only on

the class of that epoch . The process [ J ( t ) , X ( t ) ] is a bivariate

. The process J ( t ) is not

At each epoch the system regenerates in that its statistical future depends only on

the class of that epoch . The process [ J ( t ) , X ( t ) ] is a bivariate

**Markov**process. The process J ( t ) is not

**Markov**since elapsed - time information is needed to ...Page 55

The

Problems . C . A . CLAROTTI ENEA TIB - ISP ORE Casaccia - 8 . P . Anguillarese

301 , 00100 Roma , Italia 1 . - Introduction . The so - called

The

**Markov**Approach to Calculating System Reliability : ComputationalProblems . C . A . CLAROTTI ENEA TIB - ISP ORE Casaccia - 8 . P . Anguillarese

301 , 00100 Roma , Italia 1 . - Introduction . The so - called

**Markov**approach to ...Page 63

If

within reasonable limits , no alternative exists to the highly stable implicit

methods which allow the integration step size to be increased as local error

constraints get ...

If

**Markov**equations are to be numerically solved and CPU time is to be keptwithin reasonable limits , no alternative exists to the highly stable implicit

methods which allow the integration step size to be increased as local error

constraints get ...

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### 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