Modern Probability Theory: An Introductory Text BookWiley, 1981 - 256 páginas |
Contenido
Preface | 1 |
Random Variables | 20 |
Probability Space | 41 |
Derechos de autor | |
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Términos y frases comunes
A-measurable A₁ A₂ arbitrary B₁ B₂ binomial Borel function Borel sets bounded C₁ C₂ called ch.fn COMPLEMENTS AND PROBLEMS convergence in probability convergence theorem converges a.s. Corollary countable defined denoted density function dF(x distribution function dominated convergence theorem elux Example exists finite number Hence implies independent r.v.'s indicator functions inequality infinite large numbers law of large Lebesgue Lebesgue measure Lemma lim EX limit Markov martingale matrix monotone monotone convergence theorem mutually independent non-negative o-additive o-field induced o-finite P₁ Poisson probability density function probability measure probability space Proof prove random variable sequence of independent sequence of r.v.'s set function Similarly subsets takes the values transition probabilities uniformly uniquely vector w₁ w₂ X₁ X₂ Y₁ zero
Referencias a este libro
Probability Theory and Statistical Inference: Econometric Modeling with ... Aris Spanos Vista previa limitada - 1999 |