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

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Page 204

3) Values for the prior

subjectively pleasing manner. 3'2. Interpretation of prior

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**. — Inkeeping with a complete Baye- sian analysis the user should be able to, a priori,

arrive at a prior ...

Page 385

Since the mathematical form of the J-M model is familiar to those working with the

modelling of software failures, we shall in the subsequent text use the form (3.4)

and treat A and N as the unknown

Since the mathematical form of the J-M model is familiar to those working with the

modelling of software failures, we shall in the subsequent text use the form (3.4)

and treat A and N as the unknown

**parameters**. Bayesian inference for the ...Page 427

A third group of models requires strong assumptions about the general form of

reliability growth, based upon past experiences with similar processes; then one

estimates relatively few free

call ...

A third group of models requires strong assumptions about the general form of

reliability growth, based upon past experiences with similar processes; then one

estimates relatively few free

**parameters**using only failure epoch data. We willcall ...

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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