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

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Page 124

Alas, however, it may be biased. Even there, though, the true

3.14159, so this choice of estimator is not always biased. The terminology is that

it is not uniformly unbiased, and, indeed, no reasonable person would choose it.

Alas, however, it may be biased. Even there, though, the true

**value**of A may be3.14159, so this choice of estimator is not always biased. The terminology is that

it is not uniformly unbiased, and, indeed, no reasonable person would choose it.

Page 205

(3.10c) Cov [U( , tij] = n +1 Note that, by specifying the

, i = 2,...,i, for the IFR prior, one obtains a prior distribution with the expected

of the random variable equal to the prior best-guess

(3.10c) Cov [U( , tij] = n +1 Note that, by specifying the

**values**of af as «! = 1 — «?, i = 2,...,i, for the IFR prior, one obtains a prior distribution with the expected

**value**of the random variable equal to the prior best-guess

**value**for that random ...Page 439

These

successively finer

0.01, parabolic interpolation using three neighbouring

estimate ...

These

**values**were obtained tlirough an interactive numerical search oversuccessively finer

**values**of co; when the maximizing do was obtained to within0.01, parabolic interpolation using three neighbouring

**values**of co was used toestimate ...

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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