Elements of the Theory of Markov Processes and Their ApplicationsThis graduate-level text and reference in probability, with numerous applications to several fields of science, presents nonmeasure-theoretic introduction to theory of Markov processes. The work also covers mathematical models based on the theory, employed in various applied fields. Prerequisites are a knowledge of elementary probability theory, mathematical statistics, and analysis. Appendixes. Bibliographies. 1960 edition. |
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Elements of the Theory of Markov Processes and Their Applications Albert T. Bharucha-Reid Limited preview - 1997 |
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absorber Acad applications assume assumptions asymptotic birth process birth-and-death process branching processes cascade process cascade theory collision defined definition denote the number denote the probability derive determined deterministic differential equation diffusion equation diffusion processes distribution function electron-photon cascades epidemic exists expression Feller field find finite fixed fluctuation functional equation given growth Hence infinite infinitesimal initial condition integral equation introduce ionization Kendall Kolmogorov equations Laplace transform Let the random machine Markov chain Markov processes Math mathematical matrix mean and variance Mellin transform Messel Monte Carlo methods mutation neutron nonnegative nucleon nucleon cascades number of electrons number of individuals o(At obtain parameter photon Phys Poisson process population probability distribution problem Proc queueing process queueing system radiation Ramakrishnan random variable reaction recurrent satisfies solution of Eq Statist stochastic model Stochastic Processes Theorem tion transition probabilities zero