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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... Physics : Theory of Cascade Processes . 5.1 5.2 Electron - photon Cascades 5.3 Nucleon Cascades 198 213 . 223 230 235 235 . 238 265 5.4 Ionization Cascades 276 5.5 The Ramakrishnan - Srinivasan Approach to Cascade Theory 5.6 Some ...
... Physics : Theory of Cascade Processes . 5.1 5.2 Electron - photon Cascades 5.3 Nucleon Cascades 198 213 . 223 230 235 235 . 238 265 5.4 Ionization Cascades 276 5.5 The Ramakrishnan - Srinivasan Approach to Cascade Theory 5.6 Some ...
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Elements of the Theory of Markov Processes and Their Applications A. T. Bharucha-Reid Limited preview - 2012 |
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absorber Acad applications assume assumptions asymptotic birth process birth-and-death process boundary branching processes cascade process cascade theory coefficients collision consider counter defined denote the number denote the probability derive deterministic differential equation diffusion equations diffusion processes distribution function E₁ electron-photon cascades energy epidemic expression Feller finite fluctuation problem functional equation given Hence initial condition integral equation interval 0,t Introduction ionization Jánossy Kendall Kolmogorov equations Laplace transform Let the random machine Markov chain Markov processes Math mathematical matrix Mellin transform Messel method Monte Carlo methods neutron nucleon nucleon cascades number of individuals o(At obtain P₁ P₂(t photon Phys Poisson process population probability distribution Proc queueing process queueing system r₁ r₂ radiation Ramakrishnan random variable random variable X(t recurrent satisfies Statist stochastic model Stochastic Processes t₁ Theorem tion transition probabilities X₁ zero дх