Measure and Probability

This book covers the fundamentals of measure theory and probability theory. It begins with the construction of Lebesgue measure via Caratheodory?s outer measure approach and goes on to discuss integration and standard convergence theorems and contains an entire chapter devoted to complex measures, Lp spaces, Radon?Nikodym theorem, and the Riesz representation theorem. It presents the elements of probability theory, the law of large numbers, and central limit theorem. The book then discusses discrete time Markov chains, stationary distributions and limit theorems. The appendix covers many basic topics such as metric spaces, topological spaces and the Stone?Weierstrass theorem.

Probabilities and Measures. Integration. Random Variables. Probability Measures on Product Spaces. Characteristics and Convergences. Markov Chains. Some Analysis. Appendix. Tables. References. Index.