## 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 80

Page 107

... P(j(t) and the infinitesimal transition probabilities as W) = I^W-I 2 fcr(5,<-r)a^PBt

(T)dT (2.211) keB reBMBJo From Lemma 2.7 we

lim Cr(R\n),t) (2.212) (—00 for r e X — A(n), where -ft<n) = (m: m e M(?\ m < s}.

... P(j(t) and the infinitesimal transition probabilities as W) = I^W-I 2 fcr(5,<-r)a^PBt

(T)dT (2.211) keB reBMBJo From Lemma 2.7 we

**obtain**, by using (2.210), Fr(s) =lim Cr(R\n),t) (2.212) (—00 for r e X — A(n), where -ft<n) = (m: m e M(?\ m < s}.

Page 151

If we now introduce (3.89) for (7*(«;a;o) and g*t(s,x0) in the above system, we

g*(s,x0;r1,r2) = K(a>xo'>ri>fi) + K(8>xo> fj.rg), since from (3.87) G(t,x0,rltrt) = H^x^

r^) ...

If we now introduce (3.89) for (7*(«;a;o) and g*t(s,x0) in the above system, we

**obtain**(3.90) and (3.91 ). Finally, (3.92) is**obtained**from the above by noting thatg*(s,x0;r1,r2) = K(a>xo'>ri>fi) + K(8>xo> fj.rg), since from (3.87) G(t,x0,rltrt) = H^x^

r^) ...

Page 402

Now let "-fit^L (9M1 From (9.83) we

differentiating (9.82) k times and putting * = 1, we

= co, v n fw r«g - y>) - *i ,9 86) for k = 1,2, ...,m — 1; hence, the {7t satisfy a ...

Now let "-fit^L (9M1 From (9.83) we

**obtain**U0 = 1 - — — (9.85) 1 — 01 and bydifferentiating (9.82) k times and putting * = 1, we

**obtain**, by using cp(m(i(l — to))= co, v n fw r«g - y>) - *i ,9 86) for k = 1,2, ...,m — 1; hence, the {7t satisfy a ...

### What people are saying - Write a review

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

### 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