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. |
From inside the book
Results 1-3 of 86
Page 156
... distribution in B and for fixed B a continuous function in x . Then ( 9.8 ) Q ( A , B ) = √ , = √ q ( x , B ) μ ... common distribution F. Let N be a random 156 V.9 PROBABILITY DISTRIBUTIONS IN R.
... distribution in B and for fixed B a continuous function in x . Then ( 9.8 ) Q ( A , B ) = √ , = √ q ( x , B ) μ ... common distribution F. Let N be a random 156 V.9 PROBABILITY DISTRIBUTIONS IN R.
Page 279
... distribution P { X = ( − 1 ) ^ ax } = Pk . With an appropriate choice of the constants there is an overwhelming ... common distribution F and X2 * max ( X1 ,. Xn ) . Let Gn be the distribution of n ( a ) If F ( x ) = 1 - = .... e ̄ ...
... distribution P { X = ( − 1 ) ^ ax } = Pk . With an appropriate choice of the constants there is an overwhelming ... common distribution F and X2 * max ( X1 ,. Xn ) . Let Gn be the distribution of n ( a ) If F ( x ) = 1 - = .... e ̄ ...
Page 488
... common distribution , partly because of its importance , and partly to explain the essence of the method in the simplest situation . Throughout this section X1 , X2 , . . . are mutually independent variables with the common distribution ...
... common distribution , partly because of its importance , and partly to explain the essence of the method in the simplest situation . Throughout this section X1 , X2 , . . . are mutually independent variables with the common distribution ...
Contents
CHAPTER | 1 |
SPECIAL DENSITIES RANDOMIZATION | 44 |
PROBABILITY MEASURES AND SPACES | 101 |
Copyright | |
71 other sections not shown
Other editions - View all
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