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 197
... expected number of mutants in the popula- tion is easily determined . We have af -- m ( t ) = & { Y ( t ) } = μx。λ | m ( t − T ) e1a dr ( 4.99 ) where m ( t ) is the expected number of BIOLOGY 197.
... expected number of mutants in the popula- tion is easily determined . We have af -- m ( t ) = & { Y ( t ) } = μx。λ | m ( t − T ) e1a dr ( 4.99 ) where m ( t ) is the expected number of BIOLOGY 197.
Page 362
... expected on physical grounds , since the concentration of A can only decrease from its initial value x 。 to zero as the reaction proceeds . It is also of interest to remark that the expression for the expected concentration of A [ Eq ...
... expected on physical grounds , since the concentration of A can only decrease from its initial value x 。 to zero as the reaction proceeds . It is also of interest to remark that the expression for the expected concentration of A [ Eq ...
Page 468
... expected duration , 392 number of customers served , 392-393 Service times , 375 Sexes , population growth of , 175–179 Simple birth process , 77-80 Singular diffusion equations , boundary conditions for , 146 in genetics , 214n . in ...
... expected duration , 392 number of customers served , 392-393 Service times , 375 Sexes , population growth of , 175–179 Simple birth process , 77-80 Singular diffusion equations , boundary conditions for , 146 in genetics , 214n . in ...
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