## 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. |

### From inside the book

Results 1-3 of 60

Page 3

To compare the two

the population size was represented by a real-valued and continuous function of

time, while in the

To compare the two

**models**, we first observe that in the deterministic approachthe population size was represented by a real-valued and continuous function of

time, while in the

**stochastic**approach we start by assuming that the random ...Page 193

The first

and Delbriick [66]. For a discussion of these models we refer to the original

papers, and the paper of Armitage. Before considering several

of ...

The first

**stochastic models**were introduced by Lea and Coulson [64] and Luriaand Delbriick [66]. For a discussion of these models we refer to the original

papers, and the paper of Armitage. Before considering several

**stochastic models**of ...

Page 347

7.3B we consider a

is termed a "model of simple clustering." In particular, we consider the postulates

on which the model is based and discuss the functions which characterize the ...

7.3B we consider a

**stochastic model**of the spatial distribution of galaxies whichis termed a "model of simple clustering." In particular, we consider the postulates

on which the model is based and discuss the functions which characterize the ...

### What people are saying - Write a review

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

### Contents

Preface | 1 |

Processes Continuous In Space and Time | 3 |

Processes Discrete in Space and Time | 9 |

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 assume assumptions asymptotic birth process birth-and-death process cascade process cascade theory coefficient collision consider counter defined denote the number denote the probability derive determined deterministic differential equation diffusion equations diffusion processes discrete branching process distribution function electron-photon cascades epidemic exists expression extinction Feller finite fluctuation functional equation gambler's ruin given Hence independent initial condition integral equation interval introduce ionization Kendall Kolmogorov equations Laplace transform Laplace-Stieltjes transform Let the random limit theorems machine Markov chain Markov processes Math mathematical matrix Mellin transform method Monte Carlo methods neutron nonnegative nucleon nucleon cascades number of individuals o(At obtain photon Poisson process population positive probability distribution problem Proc Px(t queueing process queueing system radiation Ramakrishnan random variable random variable X(t random walk recurrent refer satisfies sequence Statist stochastic model Stochastic Processes tion transition probabilities zero