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 9
... argument is general and the Markov property remains meaningful when time is replaced by some other parameter . Examples . ( a ) Tensile strength . To obtain a continuous analogue to the proverbial finite chain whose strength is that of ...
... argument is general and the Markov property remains meaningful when time is replaced by some other parameter . Examples . ( a ) Tensile strength . To obtain a continuous analogue to the proverbial finite chain whose strength is that of ...
Page 11
... argument looks like a new derivation of the Poisson distribution but in reality it merely rephrases the original derivation of 1 ; VI , 6 in terms of random variables . For an intuitive description consider chance occurrences ( such as ...
... argument looks like a new derivation of the Poisson distribution but in reality it merely rephrases the original derivation of 1 ; VI , 6 in terms of random variables . For an intuitive description consider chance occurrences ( such as ...
Page 79
... argument except that derivatives are replaced by differences . Proof of the lemma . Assume first that no coefficient a , vanishes . We begin by showing that the functions fr have no zeros . Assume the contrary . There exists then an ...
... argument except that derivatives are replaced by differences . Proof of the lemma . Assume first that no coefficient a , vanishes . We begin by showing that the functions fr have no zeros . Assume the contrary . There exists then an ...
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