This textbook is an introduction to probability theory using measure theory. It is designed for graduate students in a variety of fields (mathematics, statistics, economics, management, finance, computer science, and engineering) who require a working knowledge of probability theory that is mathematically precise, but without excessive technicalities. The text provides complete proofs of all the essential introductory results. Nevertheless, the treatment is focused and accessible, with the measure theory and mathematical details presented in terms of intuitive probabilistic concepts, rather than as separate, imposing subjects. The text strikes an appropriate balance, rigorously developing probability theory while avoiding unnecessary detail.
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This textbook is an introduction to probability theory using measure theory. It is designed for graduate students in a variety of fields (mathematics, statistics, economics, management, finance, computer science, and engineering) who require a working knowledge of probability theory that is mathematically precise, but without excessive technicalities. The text provides complete proofs of all the essential introductory results. Nevertheless, the treatment is focused and accessible, with the measure theory and mathematical details presented in terms of intuitive probabilistic concepts, rather than as separate, imposing subjects. The text strikes an appropriate balance, rigorously developing probability theory while avoiding unnecessary detail.
An introduction to rigorous probability theory using measure theory. It provides rigorous, complete proofs of all the essential introductory mathematical results of probability theory and measure theory. More advanced or specialized areas are entirely omitted or only hinted at. For example, the text includes a complete proof of the classical central limit theorem, including the necessary continuity theorem for characteristic functions, but the more general Lindeberg central limit theorem is only outlined and is not proved. Similarly, all necessary facts from measure theory are proved before they are used, but more abstract or advanced measure theory results are not included. Furthermore, measure theory is discussed as much as possible purely in terms of probability, as opposed to being treated as a separate subject which must be mastered before probability theory can be understood.
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Soft cover. Condición: Near Fine. Near Fine/Fine/, 2003 Ilustrador. 1st Edition. Near Fine/Fine/, $80.77 9810243227 a FIRST LOOK at RIGOROUS PROBABILITY THEORY * ROSENTHAL, Jeffrey S. World Scientific Publishing Company 2003 1sT Edition, 2nD Printing S/c Glossy Platinum Grey Spine With Title In 0ff~White And Black Letters Soft Cover B00K: Near Fine/Fine, Slight Shelf, Edge And Corner Wear. Front Cover And Up To Page 33, Have Some Rippling, Due To Exposure To Moisture, No Staining, No Odor, And Not Stuck Together. 177 Numbered Pages That Are Printed On 0ff~White Paper In Near Fine/ Condition, Clean And Tight To The Spine. Prior Owner`s Name Written In Front. D/j: None. = No Odors, No Writing, No Names, Small Amount Of Rippling, Not Stuck Together, No Book Plate, Not X~Library, No Remainder Or Other Marks. This Item Will Be Sent Wrapped In Plastic, Taped Shut And In A = Padded Mailing Envelope = To Prevent Shipping Damage So That It Will Arrive In The Description Described. Description Applies To This B00K, Only. = This B00K Is Hard To Find, Will Be Packaged And Shipped Carefully, = To Avoid Shipping Damage And Will Make It, An Excellent Addition To Your Own Personal Library Collection, Or As A Gift, For The Discriminating Reader / Collector. = WORLD WIDE SHIPPING, AVAILABLE *. Nº de ref. del artículo: 016525
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