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

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Page 197

Furthermore, since for any m distinct time points z1,...,zm the estimators {h*%), h*(

zm)} are asymptotically independent, the graph of {h*{z), z>0} will exhibit wild

Furthermore, since for any m distinct time points z1,...,zm the estimators {h*%), h*(

zm)} are asymptotically independent, the graph of {h*{z), z>0} will exhibit wild

**fluctuations**, prohibiting the use of h* as an estimator of h. This behavior of h* is ...Page 264

Thus, in the case of infrequent, high-value losses, the companies exchange

these

to be if/2 than M. In fact, « the more the merrier », because the individual

variances ...

Thus, in the case of infrequent, high-value losses, the companies exchange

these

**fluctuations**for a more regular situation in which any payment is more likelyto be if/2 than M. In fact, « the more the merrier », because the individual

variances ...

Page 460

... thus in the amount of resources needed, while the other (the « secondary task

») is always kept identical, performance in the latter should show

function of the « common resources » allocated to and consumed by the former.

... thus in the amount of resources needed, while the other (the « secondary task

») is always kept identical, performance in the latter should show

**fluctuations**as afunction of the « common resources » allocated to and consumed by the former.

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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