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 86
Page 10
... stochastic process { X „ , n = 0 , 1 , 2 , . . . } , that is , a family of random variables , defined on the space X of all possible values that the random variables can assume . The space X is called the state space of the process ...
... stochastic process { X „ , n = 0 , 1 , 2 , . . . } , that is , a family of random variables , defined on the space X of all possible values that the random variables can assume . The space X is called the state space of the process ...
Page 264
... random variables . The characteristic functional , which appears to be a very powerful tool for studying the ... variable X ( E ; t ) denote the number of particles at thickness t with energy greater than E ; then , as before , we denote by • ...
... random variables . The characteristic functional , which appears to be a very powerful tool for studying the ... variable X ( E ; t ) denote the number of particles at thickness t with energy greater than E ; then , as before , we denote by • ...
Page 441
... random variables . In this case we obtain the result : The generating function of the sum of N independent random variables is the N - fold product of the generating function associated with each random variable . Compound Distributions ...
... random variables . In this case we obtain the result : The generating function of the sum of N independent random variables is the N - fold product of the generating function associated with each random variable . Compound Distributions ...
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 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 epidemic expression Feller finite fluctuation problem functional equation given Hence initial condition integral equation interval 0,t ionization Jánossy 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 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 t₂ Takács Theorem tion transition probabilities X₁ zero дх