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

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Page 25

one random variable elementary events 8 = {co} compound events A = {oolco e A

} random variable X;S -+8t (taking co -> oo(co)) A

so-called

one random variable elementary events 8 = {co} compound events A = {oolco e A

} random variable X;S -+8t (taking co -> oo(co)) A

**Poisson**process. Consider theso-called

**Poisson**process describing, say, a stream of particles emitted at ...Page 139

G) The

in which we measure n1 events in time t1 and wish an unbiased estimator for the

number of events to be observed in a later interval t2 . Call this number n2, and ...

G) The

**Poisson**estimator. In the text we discussed the case of a**Poisson**processin which we measure n1 events in time t1 and wish an unbiased estimator for the

number of events to be observed in a later interval t2 . Call this number n2, and ...

Page 399

It is well known that the partition of

means of a binomial process leads to two independent

follows that the number of undetected errors at time t, n0(t) = N— n(t), is

XQ), ...

It is well known that the partition of

**Poisson**-distributed random integer $ bymeans of a binomial process leads to two independent

**Poisson**processes. Itfollows that the number of undetected errors at time t, n0(t) = N— n(t), is

**Poisson**(XQ), ...

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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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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