An introduction to probability theory and its applications, Volume 2

applies arbitrary argument assume asymptotic atoms backward equation Baire functions Borel sets bounded calculations central limit theorem characteristic function coefficients common distribution compound Poisson condition consider constant continuous function convergence convolution covariance defined definition denote derived distribution concentrated distribution F distribution function equals example exists exponential distribution F{dx finite interval fixed follows formula Fourier given hence implies independent random variables inequality infinitely divisible integral integrand Laplace transform left side lemma limit distribution linear Markov martingale matrix measure mutually independent normal density normal distribution notation obvious operator parameter Poisson process positive probabilistic probability distribution problem proof prove random walk relation renewal equation renewal process result right side sample space satisfies semi-group sequence shows solution stable distributions stochastic kernel stochastic processes symmetric tends theory transition probabilities uniformly unique variance vector zero expectation