An Introduction to Probability Theory and Its Applications, Volume 2Wiley, 1950 - 669 pàgines 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. |
Continguts
CHAPTER | 1 |
Waiting Time Paradoxes The Poisson Process | 11 |
Waiting Times and Order Statistics | 19 |
Copyright | |
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An Introduction to Probability Theory and Its Applications, Volume 2 William Feller Previsualització limitada - 1991 |
An Introduction to Probability Theory and Its Applications, Volum 2 William Feller Visualització de fragments - 1950 |
An Introduction to Probability Theory and Its Applications, Volum 2 William Feller Visualització de fragments - 1950 |
Frases i termes més freqüents
a₁ applies arbitrary argument assume asymptotic atoms backward equation Baire functions Borel sets bounded central limit theorem characteristic function common distribution compound Poisson condition consider constant continuous function convergence convolution defined definition denote derived distribution concentrated distribution F distribution function equals example exists exponential distribution F{dx F{dy finite interval fixed follows formula Fourier given hence implies independent random variables inequality infinitely divisible integral Laplace transform law of large left side lemma Let F limit distribution limit theorem Markov martingale measure mutually independent normal distribution notation operator parameter Poisson process positive probabilistic probability distribution problem proof prove random walk renewal epochs renewal equation renewal process S₁ sample space satisfies semi-group sequence shows solution stable distributions stochastic stochastic kernel symmetric T₁ tends theory transition probabilities uniformly unique variance vector X₁ Y₁