## Proceedings of the International School of Physics "Enrico Fermi", Volume 94 |

### From inside the book

Results 1-3 of 92

Page 90

How to

the discrete state x and probability density function of Un , . . . , Un , V1 , . . . , Vn at

time t , with the understanding that , if x = 0 ( x = 1 ) , Wi is certainly zero ( v ; is ...

How to

**obtain**probability ( 1 . 2 ) . Let pc ( u ; v ; t ) denote the joint probability ofthe discrete state x and probability density function of Un , . . . , Un , V1 , . . . , Vn at

time t , with the understanding that , if x = 0 ( x = 1 ) , Wi is certainly zero ( v ; is ...

Page 317

3 )

maximum - likelihood method , etc . , 4 )

estimated values of the parameters in the chosen model , 5 ) perform goodness -

of ...

3 )

**obtain**estimates of parameters of the model using the least - squares ormaximum - likelihood method , etc . , 4 )

**obtain**the fitted model by substituting theestimated values of the parameters in the chosen model , 5 ) perform goodness -

of ...

Page 385

Bayesian inference for the unknown parameters N and A involves assigning a

prior distribution to the pair ( N , 1 ) , and then

distribution using T2 , . . . , Tx and the Bayes theorem . Our uncertainty in

knowledge ...

Bayesian inference for the unknown parameters N and A involves assigning a

prior distribution to the pair ( N , 1 ) , and then

**obtaining**the resultant posteriordistribution using T2 , . . . , Tx and the Bayes theorem . Our uncertainty in

knowledge ...

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### Contents

STATISTICAL THEORY OF RELIABLITY | 8 |

Definitions and characterizations | 12 |

J KEILSON Stochastic models in reliability theory | 23 |

Copyright | |

37 other sections not shown

### Common terms and phrases

analysis application approach associated assume assumption BARLOW Bayesian calculation called complex components consider constant continuous correctness Course defined density depends derived described detected determine discussed distribution edited epochs equations equivalence ergodic errors estimate example exists expected exponential fact fail failure rate fault function given Hence important increasing independent input integration interest interval known likelihood limit Markov matrix mean measure method modules normal Note observed obtain occur operational parameters performance phase positive possible posterior predictive prior probability problem procedure prove random variables renewal repair requirements rule sample selected sequence simple software reliability space specification statistical stochastic structure Suppose task theorem theory tion transition tree University values York