## 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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Page 49

We now give a result, due to Sevastyanov, which establishes a relationship

between the first-moment matrix M and the probability that the population will

eventually

population ...

We now give a result, due to Sevastyanov, which establishes a relationship

between the first-moment matrix M and the probability that the population will

eventually

**become**extinct. Let ps”(0, 0,..., 0) denote the probability that thepopulation ...

Page 70

If the column vector (P1,0t), Pa,(t), . . .), for j fixed, satisfies (2.69) and (2.70), then

P.,(t) > Fu(t). Proof: We only consider the proof of part 1, since the proof for part 2

follows similar lines. Let Pu(t) = Z(t) for i fixed. Then (2.69)

If the column vector (P1,0t), Pa,(t), . . .), for j fixed, satisfies (2.69) and (2.70), then

P.,(t) > Fu(t). Proof: We only consider the proof of part 1, since the proof for part 2

follows similar lines. Let Pu(t) = Z(t) for i fixed. Then (2.69)

**becomes***=52.0, 2,0) ...Page 218

... we see that the Gegenbauer parameters are a = i – l and b = 1; hence, (4.145)

tition (4.153) i = 1 The constants C, are determined by the initial condition X(0) ...

... we see that the Gegenbauer parameters are a = i – l and b = 1; hence, (4.145)

**becomes**X,(y) = Ti_1(y) (4.152) Therefore, (4.140)**becomes**f(t,x) = 5. CT; (9)e-tition (4.153) i = 1 The constants C, are determined by the initial condition X(0) ...

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### Contents

Introduction | 1 |

Processes Discrete in Space and Continuous in Time | 57 |

Processes Continuous in Space and Time | 129 |

Copyright | |

9 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 addition applications approach arrival associated assume assumptions becomes birth boundary branching processes called cascade coefficients collision concerned condition consider constant continuous counter death defined denote density derive described determined developed differential equation diffusion discussion distribution function electron energy epidemic equal exists expected expression finite fluctuation given gives growth Hence independent individuals initial condition integral interest interval introduce Kolmogorov equations Laplace transform length limit machine Markov Markov chain Markov processes Math mathematical mean method moments necessary nucleon obtain particle particular photon Poisson population positive primary problem Proof properties queueing radiation random variable reaction refer relation represent respectively satisfies shown simple ſº solution Statist Stochastic Processes Theorem theory tion transition probabilities zero