000			01959 a2200217 4500
003			OSt
005			20241016122142.0
008			241009b |||||||| |||| 00| 0 eng d
020			_a9789812703712
040			_aICTS-TIFR
050			_aQA273. R784
100			_aJeffery S. Rosenthal
245			_aA first look at rigorous probability theory
250			_a2nd Ed.
260			_bWorld Scientific Publishing Co. Pte. Ltd. _aSingapore: _c[c2006]
300			_a219 p.
505			_a1. The Need for Measure Theory 2. Probability Triples 3. Further Probabilistic Foundations 4. Expected Values 5. Inequalities and Convergence 6. Distributions of Random Variables 7. Stochastic Processes and Gambling Games 8. Discrete Markov Chains 9. More Probability Theorems 10 Weak Convergence 11. Characteristic Functions 12. Decomposition of Probability Laws 13. Conditional Probability and Expectation 14. Martingales 15. General Stochastic Processes
520			_aThis 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. In this new edition, many exercises and small additional topics have been added and existing ones expanded. The text strikes an appropriate balance, rigorously developing probability theory while avoiding unnecessary detail. --- summary provided by publisher
650			_aMathematics
942			_2lcc _cBK
999			_c35466 _d35466