An Introduction to Probability Theory and Its Applications, Volume 2Wiley, 1950 - Probabilities Vol. 2 has series: Wiley series in probability and mathematical statistics. Bibliographical footnotes. "Some books on cagnate subjects": v. 2, p. 615-616. |
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Page 205
... stochastic matrix with elements Pis whose rows were probability distributions . Now we shall have to consider transitions from a point x to an arbitrary interval or set I in R " ; we shall denote the probability of this transition by K ...
... stochastic matrix with elements Pis whose rows were probability distributions . Now we shall have to consider transitions from a point x to an arbitrary interval or set I in R " ; we shall denote the probability of this transition by K ...
Page 365
... STOCHASTIC PROCESSES Perhaps the most striking proof of the power of the renewal theorem is that it enables us without effort to derive the existence of a “ steady state ... STOCHASTIC PROCESSES Existence of Limits in Stochastic Processes.
... STOCHASTIC PROCESSES Perhaps the most striking proof of the power of the renewal theorem is that it enables us without effort to derive the existence of a “ steady state ... STOCHASTIC PROCESSES Existence of Limits in Stochastic Processes.
Page 616
... STOCHASTIC PROCESSES WITH EMPHASIS ON APPLICATIONS OR EXAMPLES Barucha - Reid , A. T. [ 1960 ] , Elements of the Theory of Stochastic Processes and Their Applications . McGraw - Hill , New York . 468 pp . Beneš , V. E. [ 1963 ] ...
... STOCHASTIC PROCESSES WITH EMPHASIS ON APPLICATIONS OR EXAMPLES Barucha - Reid , A. T. [ 1960 ] , Elements of the Theory of Stochastic Processes and Their Applications . McGraw - Hill , New York . 468 pp . Beneš , V. E. [ 1963 ] ...
Contents
CHAPTER | 1 |
SPECIAL DENSITIES RANDOMIZATION | 44 |
PROBABILITY MEASURES AND SPACES | 101 |
Copyright | |
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An Introduction to Probability Theory and Its Applications, Volume 2 William Feller Limited preview - 1991 |
Common terms and phrases
a₁ applies arbitrary argument assume asymptotic atoms backward equation Baire functions Borel sets bounded central limit theorem characteristic function common distribution compound Poisson condition consider constant continuous function convergence convolution defined definition denote density derived distribution F distribution function equals example exists exponential distribution F{dx finite interval fixed follows formula given hence implies independent random variables inequality infinitely divisible integral integrand Laplace transform law of large left side lemma Let F limit distribution Markov martingale measure mutually independent normal distribution notation o-algebra obvious operator parameter Poisson process positive probabilistic probability distribution problem proof prove random walk renewal epochs renewal equation renewal process S₁ sample space satisfies semi-group sequence shows solution stable distributions stochastic stochastic kernel symmetric T₁ tends theory transition probabilities uniformly unique variance vector X₁ Y₁ zero expectation