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Elementary Probability Theory with Stochastic Processes

Elementary Probability Theory with Stochastic Processes
Catalogue Information
Field name Details
Dewey Class 519
Title Elementary Probability Theory with Stochastic Processes ([EBook]) / by Kai Lai Chung.
Author Chung, Kai Lai , 1917-2009
Other name(s) SpringerLink (Online service)
Publication New York, NY : Springer , 1974.
Physical Details X, 325 pages, 9 illus. : online resource.
Series Undergraduate texts in mathematics 0172-6056
ISBN 9781475739732
Summary Note In the past half-century the theory of probability has grown from a minor isolated theme into a broad and intensive discipline interacting with many other branches of mathematics. At the same time it is playing a centrat role in the mathematization of various applied sciences such as statistics, Opera­ tions research, biology, economics and psychology-to name a few to which the prefix "mathematical" has so far been firmly attached. The coming-of-age of probability has been reflected in the change of contents of textbooks on the subject. In the old days most of these books showed a visible split­ personality torn between the combinatorial games of chance and the so-called "theory of errors" centering in the normal distribution. This period ended with the appearance of Feller's dassie treatise (see [Feiler I]t) in 1950, from the manuscript of which I gave my first substantial course in probability. With the passage of time probability theory and its applications have won a place in the college curriculum as a mathematical discipline essential to many fields of study. The elements of the theory are now given at different Ievels, sometimes even before calculus. The present textbook is intended for a course at about the sophomore Ievel. It presupposes no prior acquaintance with the subject and the first three chapters can be read largely without the benefit of calculus.:
Contents note 1: Set -- 2: Probability -- 3: Counting -- 4: Random Variables -- 5: Conditioning and Independence -- 6: Mean, Variance and Transforms -- 7: Poisson and Normal Distributions -- 8: From Random Walks to Markov Chains -- General References -- Answers to Problems.
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