## Elements of the theory of Markov processes and their applicationsGraduate-level text and reference in probability, with numerous scientific applications. Nonmeasure-theoretic introduction to theory of Markov processes and to mathematical models based on the theory. Appendixes. Bibliographies. 1960 edition. |

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Results 1-3 of 22

Page 299

The theory of particle

stochastic processes associated with the ... The general problem can be

described as follows: Consider a

substance.

The theory of particle

**counters**is concerned with the formulation and study ofstochastic processes associated with the ... The general problem can be

described as follows: Consider a

**counter**placed within range of a radioactivesubstance.

Page 305

In applying the general theory to Type II

distribution function F(t) is unknown, since by ... This condition will obtain if a

large number of impulses arrive at the

first ...

In applying the general theory to Type II

**counters**we first observe that thedistribution function F(t) is unknown, since by ... This condition will obtain if a

large number of impulses arrive at the

**counter**in rapid succession following thefirst ...

Page 307

C. General Model of a Particle

theory of particle

...

C. General Model of a Particle

**Counter**. In Sec. 6.3B we considered the generaltheory of particle

**counters**and the application of this theory to Type I and Type II**counters**. We now consider a general model of a particle**counter**which includes...

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### Contents

Introduction | 1 |

Processes Discrete in Space and Time | 9 |

Processes Discrete in Space and Continuous in Time | 57 |

Copyright | |

10 other sections not shown

### Other editions - View all

Elements of the Theory of Markov Processes and Their Applications A. T. Bharucha-Reid Limited preview - 2012 |

Elements of the Theory of Markov Processes and Their Applications Albert T. Bharucha-Reid Limited preview - 1997 |

### Common terms and phrases

absorber Acad applications associated assume assumptions asymptotic birth process birth-and-death process branching processes cascade process cascade theory coefficient collision consider defined denote the number denote the probability derive determined deterministic differential equation diffusion equations diffusion processes distribution function electron-photon cascades epidemic exists expression Feller finite fluctuation problem functional equation given Hence initial condition integral equation interval ionization Kendall Kolmogorov equations Laplace transform Laplace-Stieltjes transform Let the random machine Markov chain Markov processes Math mathematical matrix mean and variance mean number Mellin transform Messel method Monte Carlo methods mutation neutron nonnegative nucleon nucleon cascades number of electrons number of individuals o(At obtain parameter photon Phys Poisson process probability distribution Proc Px(t queueing process queueing system radiation Ramakrishnan random variable random variable X(t reaction recurrent refer satisfies solution of Eq Statist stochastic model Stochastic Processes Theorem tion transition probabilities zero