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

### From inside the book

Results 1-3 of 12

Page 32

The process J(t) is said to be semi-Markov. When all the transition rate functions r

\lk(x) are constants, i.e. independent of x, the process J(t) is Markov. The process

J(t) is then said to be a finite

The process J(t) is said to be semi-Markov. When all the transition rate functions r

\lk(x) are constants, i.e. independent of x, the process J(t) is Markov. The process

J(t) is then said to be a finite

**Markov chain**in continuous time, and will be ...Page 37

between the continuous-time chain N(t) and the discrete-time chain J(k) of

considerable algorithmic and theoretical value. ... If the finite

irreducible and hence ergodic, it is often of importance to know when steady-

state ...

between the continuous-time chain N(t) and the discrete-time chain J(k) of

considerable algorithmic and theoretical value. ... If the finite

**Markov chain**J(t) isirreducible and hence ergodic, it is often of importance to know when steady-

state ...

Page 38

If the failure times and repair times for Gr are exponentially distributed with rate fj,

T and A,, respectively, then each component may be modeled as a

Ir(t) in continuous time having two states, lr in the working state and 0r the failed ...

If the failure times and repair times for Gr are exponentially distributed with rate fj,

T and A,, respectively, then each component may be modeled as a

**Markov chain**Ir(t) in continuous time having two states, lr in the working state and 0r the failed ...

### What people are saying - Write a review

We haven't found any reviews in the usual places.

### Contents

System Eeliabujty | 3 |

Statistical Theory of Eeliablitt | 8 |

Definitions and characterizations | 12 |

Copyright | |

39 other sections not shown

### Other editions - View all

### 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