Number-theoretic methods in statistics

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James J. Callahan

With a fresh geometric approach that incorporates more than 250 illustrations, this textbook sets itself apart from all others in advanced calculus. Besides the classical capstones--the change of variables formula, implicit and inverse function theorems, the integral theorems of Gauss and Stokes--the text treats other important topics in differential analysis, such as Morse's lemma and the Poincaré lemma. The ideas behind most topics can be understood with just two or three variables. This invites geometric visualization; the book incorporates modern computational tools to give visualization real power. Using 2D and 3D graphics, the book offers new insights into fundamental elements of the calculus of differentiable maps, such as the role of the derivative as the local linear approximation to a map and its role in the change of variables formula for multiple integrals. The geometric theme continues with an analysis of the physical meaning of the divergence and the curl at a level of detail not found in other advanced calculus books. Advanced Calculus: A Geometric View is a textbook for undergraduates and graduate students in mathematics, the physical sciences, and economics. Prerequisites are an introduction to linear algebra and multivariable calculus. There is enough material for a year-long course on advanced calculus and for a variety of semester courses--including topics in geometry. It avoids duplicating the material of real analysis. The measured pace of the book, with its extensive examples and illustrations, make it especially suitable for independent study.

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James E. Gentle

Monte Carlo simulation has become one of the most important tools in all fields of science. Simulation methodology relies on a good source of numbers that appear to be random. These "pseudorandom" numbers must pass statistical tests just as random samples would. Methods for producing pseudorandom numbers and transforming those numbers to simulate samples from various distributions are among the most important topics in statistical computing. This book surveys techniques of random number generation and the use of random numbers in Monte Carlo simulation. The book covers basic principles, as well as newer methods such as parallel random number generation, nonlinear congruential generators, quasi Monte Carlo methods, and Markov chain Monte Carlo. The best methods for generating random variates from the standard distributions are presented, but also general techniques useful in more complicated models and in novel settings are described. The emphasis throughout the book is on practical methods that work well in current computing environments. The book includes exercises and can be used as a test or supplementary text for various courses in modern statistics. It could serve as the primary test for a specialized course in statistical computing, or as a supplementary text for a course in computational statistics and other areas of modern statistics that rely on simulation. The book, which covers recent developments in the field, could also serve as a useful reference for practitioners. Although some familiarity with probability and statistics is assumed, the book is accessible to a broad audience. The second edition is approximately 50% longer than the first edition. It includes advances in methods for parallel random number generation, universal methods for generation of nonuniform variates, perfect sampling, and software for random number generation.

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Victor Havin, Burglind Jöricke

This Ergebnisse volume is devoted to the Uncertainty Principle (UP) and it contains a collection of essays dealing with the various manifestations of this phenomenon. The authors describe different approaches to the subject, using both "real" and "complex" techniques and succeed to show the influence of the UP in some areas outside Fourier Analysis. The book is essentially self-contained and thus accessible to any graduate student acquainted with the fundamentals of Fourier, Complex and Functional Analysis. As there is no other book approaching the subject of UP in the way Havin and Joericke do in this work, this book will certainly be a welcome addition to the bookshelves of many researchers working in this field.

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Masuo Suzuki

This volume is the proceedings of the Ninth Taniguchi Symposium on the Theory of Condensed Matter. It presents the basic ideas of quantum statistical Monte Carlo methods, including generalizations of the standard methods, and discusses the important problem of convergence. In addition, some promising powerful new methods are proposed, for example, a transfer-matrix method. Further contributions describe specific applications to quantum spin and fermion systems at finite temperatures, such as the treatment of randomness and frustration in a triangular antiferromagnet, the investigation of critical properties of spin chains, the study of spin glasses and the simulation of polaron and bipolaron systems. In particular, some new information is given about frustrated quantum spin systems and the dense Kondo problem.