This very well written and accessible book emphasizes the reasons for studying measure theory, which is the foundation of much of probability. By focusing on measure, many illustrative examples and applications, including a thorough discussion of standard probability distributions and densities, are opened. The book also includes many problems and their fully worked solutions.

An introduction to analysis with the right mix of abstract theories and concrete problems. Starting with general measure theory, the book goes on to treat Borel and Radon measures and introduces the reader to Fourier analysis in Euclidean spaces with a treatment of Sobolev spaces, distributions, and the corresponding Fourier analysis. It continues with a Hilbertian treatment of the basic laws of probability including Doob's martingale convergence theorem and finishes with Malliavin's "stochastic calculus of variations" developed in the context of Gaussian measure spaces. This invaluable contribution gives a taste of the fact that analysis is not a collection of independent theories, but can be treated as a whole.

The 'converging factor' for an asymptotic series representing a function f(x) is that number by which the (n + 1) term of the series must be multiplied so that the result of adding this product to the sum of the first n terms will be f(x). The determination to high precision of this factor for the asymptotic series representing the probability integral is described. Tables of this factor to 63 decimal places are included for n ranging from 2 to 64. (Author).

An examination of the theory of correlation and correlation tables.