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 90
Page vii
... theory may illustrate this point . Chapter IV contains an informal introduction to the basic ideas of measure theory and the conceptual foundations of probability . The same chapter lists the few facts of measure theory used in the ...
... theory may illustrate this point . Chapter IV contains an informal introduction to the basic ideas of measure theory and the conceptual foundations of probability . The same chapter lists the few facts of measure theory used in the ...
Page 101
... theory depend on messy technical details . For the beginner and outsider access is made difficult also by the many facets and uses of measure theory ; excellent introductions exist , but of necessity they dwell on great generality and ...
... theory depend on messy technical details . For the beginner and outsider access is made difficult also by the many facets and uses of measure theory ; excellent introductions exist , but of necessity they dwell on great generality and ...
Page 311
... theory is the connecting link between these topics . The existence of generators will be proved only in chapter XIII . In theory the present exposition might have covered the processes and semi - groups of the last chapter as a special ...
... theory is the connecting link between these topics . The existence of generators will be proved only in chapter XIII . In theory the present exposition might have covered the processes and semi - groups of the last chapter as a special ...
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