Summary :

lt;Pgt;This is a graduate level textbook on measure theory and probability theory. The book can be used as a text for a two semester sequence of courses in measure theory and probability theory, with an option to include supplemental material on stochastic processes and special topics. It is intended primarily for first year Ph.D. students in mathematics and statistics although mathematically advanced students from engineering and economics would also find the book useful. Prerequisites are kept to the minimal level of an understanding of basic real analysis concepts such as limits, continuity, differentiability, Riemann integration, and convergence of sequences and series. A review of this material is included in the appendix.lt;/Pgt; lt;Pgt;The book starts with an informal introduction that provides some heuristics into the abstract concepts of measure and integration theory, which are then rigorously developed. The first part of the book can be used for a standard real analysis course for both mathematics and statistics Ph.D. students as it provides full coverage of topics such as the construction of LebesgueStieltjes measures on real line and Euclidean spaces, the basic convergence theorems, L^p spaces, signed measures, RadonNikodym theorem, Lebesgueapos;s decomposition theorem and the fundamental theorem of Lebesgue integration on R, product spaces and product measures, and FubiniTonelli theorems. It also provides an elementary introduction to Banach and Hilbert spaces, convolutions, Fourier series and Fourier and Plancherel transforms. Thus part I would be particularly useful for students in a typical Statistics Ph.D. program if a separate course on real analysis is not a standard requirement.lt;/Pgt; lt;Pgt;Part II (chapters 613) provides full coverage of standard graduate level probability theory. It starts with Kolmogorovapos;s probability model and Kolmogorovapos;s existence theorem. It then treats thoroughly the laws of large numbers including renewal theory and ergodic theorems with applications and then weak convergence of probability distributions, characteristic functions, the LevyCramer continuity theorem and the central limit theorem as well as stable laws. It ends with conditional expectations and conditional probability, and an introduction to the theory of discrete time martingales.lt;/Pgt; lt;Pgt;Part III (chapters 1418) provides a modest coverage of discrete time Markov chains with countable and general state spaces, MCMC, continuous time discrete space jump Markov processes, Brownian motion, mixing sequences, bootstrap methods, and branching processes. It could be used for a topics/seminar course or as an introduction to stochastic processes.lt;/Pgt; lt;Pgt;From the reviews: lt;/Pgt; lt;Pgt;quot;...There are interesting and nonstandard topics that are not usually included in a first course in measturetheoretic probability including Markov Chains and MCMC, the bootstrap, limit theorems for martingales and mixing sequences, Brownian motion and Markov processes. The material is wellsuported with many endofchapter problems.quot; D.L. McLeish for Short Book Reviews of the ISI, December 2006lt;/Pgt;
