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

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Page 123

For example, it seems reasonable that the

distribution G, should bo A, and not something else. That condition is called

unbiasedness. To be useful, the asymptotic value of 8, as N increases, should

approach A, ...

For example, it seems reasonable that the

**expected value**of S, given thedistribution G, should bo A, and not something else. That condition is called

unbiasedness. To be useful, the asymptotic value of 8, as N increases, should

approach A, ...

Page 124

Even there, though, the true value of A may be 3.14159, so this choice of

estimator is not always biased. ... It is, however, easy to show that the

ikf a> ...

Even there, though, the true value of A may be 3.14159, so this choice of

estimator is not always biased. ... It is, however, easy to show that the

**expectation****value**of M is given by <ilf> = a/2, as expected, and that the variance is given by <ikf a> ...

Page 205

(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

of the random variable equal to the prior best-guess value for that random ...

(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 ...

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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