Davidson J. Stochastic Limit Theory

Springer-Verlag London Limited, 2005; 485 pages. Probability and statistics are fascinating subjects on the interface between mathematics and applied sciences that help us understand and solve practical problems. We believe that you, by learning how stochastic methods come about and why they work, will be able to understand the meaning of statistical statements as well as judge the quality of their content, when facing such problems on your own....

MIT, 2000. - 284 pages. An intuitive, yet precise introduction to probability theory, stochastic processes, and probabilistic models used in science, engineering, economics, and related fields. This is the currently used textbook for "Probabilistic Systems Analysis, " an introductory probability course at the Massachusetts Institute of Technology, attended by a large number of undergraduate and graduate students. The book covers the fundamentals...

Brooks/Cole, 2005. - 497 Pages. This classic introduction to probability theory for beginning graduate students covers laws of large numbers, central limit theorems, random walks, martingales, Markov chains, ergodic theorems, and Brownian motion. It is a comprehensive treatment concentrating on the results that are the most useful for applications. Its philosophy is that the best way to learn probability is to see it in action, so there are 200...

Duxbury Press, 2004. - 512 Pages. This classic introduction to probability theory for beginning graduate students covers laws of large numbers, central limit theorems, random walks, martingales, Markov chains, ergodic theorems, and Brownian motion. It is a comprehensive treatment concentrating on the results that are the most useful for applications. Its philosophy is that the best way to learn probability is to see it in action, so there are 20...

Cambridge University Press, 2010. - 448 pages. Fourth Edition This book is an introduction to probability theory covering laws of large numbers, central limit theorems, random walks, martingales, Markov chains, ergodic theorems, and Brownian motion. It is a comprehensive treatment concentrating on the results that are the most useful for applications. Its philosophy is that the best way to learn probability is to see it in action, so there are...

John Wiley & Sons, 1963. - 695 pages. Probability theory can be understood as a mathematical model for the intuitive notion of uncertainty. Without probability theory all the stochastic models in Physics, Biology, and Economics would either not have been developed or would not be rigorous. Also, probability is used in many branches of pure mathematics, even in branches one does not expect this, like in convex geometry.

Prentice Hall, 2004. - 644 pages. Presenting probability in a natural way, this book uses interesting, carefully selected instructive examples that explain the theory, definitions, theorems, and methodology. Fundamentals of Probability has been adopted by the American Actuarial Society as one of its main references for the mathematical foundations of actuarial science. Topics include: axioms of probability; combinatorial methods; conditional...

Overseas, 2009. - 373 pages. Chapter 1-2 of this text covers material of a basic probability course. Chapter 3 deals with discrete stochastic processes including Martingale theory. Chapter 4 covers continous time stochastic processes like Brownian motion and stochastic differential equations. The last chapter selected topics got considerably extended in the summer of 2006. In the original course, only localization and percolation problems were...

Springer-Verlag, 2010. - 436 pages. This textbook on applied probability is intended for graduate students in applied mathematics, biostatistics, computational biology, computer science, physics, and statistics. It presupposes knowledge of multivariate calculus, linear algebra, ordinary differential equations, and elementary probability theory. Given these prerequisites, Applied Probability presents a unique blend of theory and applications, wi...

Springer, 2009. - 340 pages. This book presents elementary probability theory with interesting and well-chosen applications that illustrate the theory. An introductory chapter reviews the basic elements of differential calculus which are used in the material to follow. The theory is presented systematically, beginning with the main results in elementary probability theory. This is followed by material on random variables. Random vectors, includi...