An introduction to probability theory and its applicationsThe exponential and the uniform densities; Special densities. Randomization; Densities in higher dimensions. Normal densities and processes; Probability measures and spaces; Probability distributions in Rr; A survey of some important distributions and processes; Laws of large numbers. Aplications in analysis; The basic limit theorems; Infinitely divisible distributions and semi-groups; Markov processes and semi-groups; Renewal theory; Random walks in R1; Laplace transforms. Tauberian theorems. Resolvents; Aplications of Laplace transforms; Characteristic functions; Expansions related to the central limit theorem; Infinitely divisible distributions; Applications of Fourier methods to ramdom walks; harmonic analysis; Answers to problems. |
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LibraryThing Review
User Review - redgiant - LibraryThingIf you were to lock me up for a year and allow only one book for the whole time, this is the book I would take with me. The way each problem is treated is delightful. The book is slightly dated and so ... Read full review
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User Review - bluetyson - LibraryThingA really, reall dull mathematics text. An important book, but this one you will not be pleased with having to read, or at least I never came across anyone that was, when I had to use it. Highly detailed and quite complex look at the probability subject for the tertiary level beginner. Read full review
Contents
The Exponential and the Uniform Densities | 1 |
Densities Convolutions | 3 |
The Exponential Density | 8 |
Copyright | |
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Common terms and phrases
applies arbitrary argument assume atoms Baire functions Borel sets bounded central limit theorem common distribution conditional density consider continuous function converges convolution coordinate variables defined definition denote derived distributed uniformly distribution F distribution function equals event example exists exponential distribution finite interval fixed follows formula given hence identity implies independent random variables independent variables inequality infinitely divisible integral ladder Laplace transform large numbers law of large lemma length limit theorem linear Markov Markovian martingale matrix measure monotone mutually independent normal density normal distribution notation obvious operator parameter Poisson process positive probabilistic probability distribution probability space problem proof prove random walk renewal equation renewal process result sample space satisfying semi-group sequence solution stable distribution stationary stochastic processes sufficiently symmetric tends theory uniform distribution unique vanishes variance vector zero expectation
Bibliographic information
| Title | An introduction to probability theory and its applications Wiley series in probability and mathematical statistics: Probability and mathematical statistics An Introduction to Probability Theory and Its Applications, William Feller Volume 81 of Wiley Series in Probability and Statistics - Applied Probability and Statistics Section Series Volume 81 of Wiley series in probability and mathematical statistics Volume 2 of Wiley publication in mathematical statistics Wiley publications in statistics |
| Author | William Feller |
| Contributor | William Feller |
| Edition | 2, illustrated |
| Publisher | Wiley, 1971 |
| Original from | the University of Michigan |
| Digitized | Oct 26, 2007 |
| ISBN | 0471257095, 9780471257097 |
| Length | 704 pages |
| Subjects | › › Business & Economics / Statistics Mathematics / Probability & Statistics / General Probabilities |
| Export Citation | BiBTeX EndNote RefMan |