## Proceedings of the International School of Physics "Enrico Fermi". |

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Page 23

familiar with elementary probability theory as represented in any introductory text

[1, 2]. It is also assumed that the reader is familiar with the elementary

classification ...

**Stochastic**models. l'l.**Stochastic**processes. - It is assumed that the reader isfamiliar with elementary probability theory as represented in any introductory text

[1, 2]. It is also assumed that the reader is familiar with the elementary

classification ...

Page 24

The totality of all such realizations constitutes the «

) where the index set {1,2, N} is finite. Actually, the situation discussed above is

simple in that the number of tosses is finite. There are occasions, however, when

...

The totality of all such realizations constitutes the «

**stochastic**process » (X1,...,X!I) where the index set {1,2, N} is finite. Actually, the situation discussed above is

simple in that the number of tosses is finite. There are occasions, however, when

...

Page 301

Among many non-error-counting models, we will briefly discuss the Goel and

Okumoto imperfect-debugging model [27] and the input-domain-based

model developed at Berkeley. 4'4.1.2.1. Goel and Okumoto imperfect-debugging

...

Among many non-error-counting models, we will briefly discuss the Goel and

Okumoto imperfect-debugging model [27] and the input-domain-based

**stochastic**model developed at Berkeley. 4'4.1.2.1. Goel and Okumoto imperfect-debugging

...

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### Contents

System Eeliabujty | 3 |

Statistical Theory of Eeliablitt | 8 |

Definitions and characterizations | 12 |

Copyright | |

39 other sections not shown

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### Common terms and phrases

algorithm approach associated assume assumption Bayesian boundary points chain coherent system complex conjugate prior consider correctness defined denote detected discussed edited equations equivalence class ergodic errors example exponential distribution failure rate Fault Tree Analysis function gamma given human reliability IEEE Trans IFEA implementation increasing independent input domain integration interval likelihood Markov Markov chain matrix mean method modules monotone month2 N. D. Singpurwalla number of failures number of system NUMITEMS observed obtained operational output parameters phase Poisson Poisson process possible predictive prior distribution probability problem procedure Proschan R. E. Barlow random variables reliability growth models reliability theory renewal theory repair requirements sample sect sequence Software Eng software reliability software reliability models specification Stat statistical stochastic stochastic process subsection system failure system reliability techniques theorem tion tt tt values vector zero