An Introduction to Probability Theory and Its Applications, Volume 2, Volúmenes 1-2Wiley, 1957 - 704 páginas The classic text for understanding complex statistical probability An Introduction to Probability Theory and Its Applications offers comprehensive explanations to complex statistical problems. Delving deep into densities and distributions while relating critical formulas, processes and approaches, this rigorous text provides a solid grounding in probability with practice problems throughout. Heavy on application without sacrificing theory, the discussion takes the time to explain difficult topics and how to use them. This new second edition includes new material related to the substitution of probabilistic arguments for combinatorial artifices as well as new sections on branching processes, Markov chains, and the DeMoivre-Laplace theorem. |
Índice
CHAPTER | 1 |
Waiting Times and Order Statistics | 19 |
Random Splittings | 25 |
Página de créditos | |
Otras 53 secciones no se muestran.
Otras ediciones - Ver todo
An Introduction to Probability Theory and Its Applications, Volúmenes 1-2 William Feller Vista de fragmentos - 1957 |
An Introduction to Probability Theory and Its Applications, Volúmenes 1-2 William Feller Vista de fragmentos - 1957 |
An Introduction to Probability Theory and Its Applications, Volúmenes 1-2 William Feller Vista de fragmentos - 1957 |
Términos y frases comunes
applies arbitrary argument assume asymptotic backward equation Baire functions bounded calculations central limit theorem characteristic function common distribution compound Poisson distribution condition consider constant continuous function convergence convolution defined definition denote derived differentiation distribution concentrated 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 ladder Laplace transform law of large left side lemma Let F limit distribution Markov measure mutually independent normal density normal distribution notation operator parameter Poisson process positive probabilistic probability distribution problem proof prove random walk renewal equation renewal process result S₁ sample space satisfies semi-group sequence shows solution stable distributions stochastic stochastic kernel symmetric tends theory transition probabilities uniformly unique variance vector X₁ Y₁ zero expectation
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Introduction To Algorithms Thomas H Cormen,Charles E Leiserson,Ronald L Rivest,Clifford Stein Vista previa restringida - 2001 |
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