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

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Page 204

3) Values for the prior parameters may be

subjectively pleasing manner. 3'2. Interpretation of prior parameters. — In

keeping with a complete Baye- sian analysis the user should be able to, a priori,

arrive at a prior ...

3) Values for the prior parameters may be

**obtained**in a straightforward,subjectively pleasing manner. 3'2. Interpretation of prior parameters. — In

keeping with a complete Baye- sian analysis the user should be able to, a priori,

arrive at a prior ...

Page 205

It is also interesting to note that the Cov (ut, u,) is a function of both i and j, which

takes into account the relative closeness of the intervals. Similar results may be

...

It is also interesting to note that the Cov (ut, u,) is a function of both i and j, which

takes into account the relative closeness of the intervals. Similar results may be

**obtained**for the DFE assumption. 4. - Posterior analysis. If we assume a squared...

Page 206

i-i In order to completely specify these distributions, the constant of integration

must be

integrating (4.3) and (4.4) over Ul and UD, respectively, these constants are

i-i In order to completely specify these distributions, the constant of integration

must be

**obtained**. By expanding the (1 — «,)" terms into a binomial series andintegrating (4.3) and (4.4) over Ul and UD, respectively, these constants are

**obtained**...### What people are saying - Write a review

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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