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 3
... model is a special case of a stochastic model , in the sense that it yields results which hold with probability one . We close this brief discussion of deterministic and stochastic models by considering the mean or expected population ...
... model is a special case of a stochastic model , in the sense that it yields results which hold with probability one . We close this brief discussion of deterministic and stochastic models by considering the mean or expected population ...
Page 193
... stochastic models were introduced by Lea and Coulson [ 64 ] and Luria and Delbrück [ 66 ] . For a discussion of ... model for a single mutation process with equal growth rates for the normal and mutant populations , and in Sec . 4.3C we ...
... stochastic models were introduced by Lea and Coulson [ 64 ] and Luria and Delbrück [ 66 ] . For a discussion of ... model for a single mutation process with equal growth rates for the normal and mutant populations , and in Sec . 4.3C we ...
Page 347
... stochastic model of the spatial distribution of galaxies which is termed a “ model of simple clustering . " In par- ticular , we consider the postulates on which the model is based and discuss the functions which characterize the ...
... stochastic model of the spatial distribution of galaxies which is termed a “ model of simple clustering . " In par- ticular , we consider the postulates on which the model is based and discuss the functions which characterize the ...
Contents
Introduction | 1 |
Processes Discrete in Space and Time | 9 |
Processes Discrete in Space and Continuous in Time | 57 |
Copyright | |
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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 |
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absorber applications assume assumptions asymptotic birth process birth-and-death process boundary branching processes cascade process cascade theory coefficient collision consider counter defined denote the number denote the probability deterministic differential equation diffusion equations diffusion processes discrete branching process distribution function E₁ electron-photon cascades energy epidemic exists expression Feller finite functional equation given Hence initial condition integral equation interval 0,t ionization Kolmogorov equations Laplace transform Let the random machine Markov chain Markov processes Math mathematical matrix Mellin transform method Monte Carlo methods neutron nonnegative nucleon number of individuals o(At obtain P₁ photon Poisson process population probability distribution problem Proc product density queueing system r₁ radiation Ramakrishnan random variable random variable X(t random walk recurrent satisfies sequence Statist stochastic model Stochastic Processes t₁ t₂ Takács tion transition probabilities X₁ zero дх