Описание: A one-year course in probability theory and the theory of random processes, taught at Princeton University to undergraduate and graduate students, forms the core of the content of this bookIt is structured in two parts: the first part providing a detailed discussion of Lebesgue integration, Markov chains, random walks, laws of large numbers, limit theorems, and their relation to Renormalization Group theory. The second part includes the theory of stationary random processes, martingales, generalized random processes, Brownian motion, stochastic integrals, and stochastic differential equations. One section is devoted to the theory of Gibbs random fields.This material is essential to many undergraduate and graduate courses. The book can also serve as a reference for scientists using modern probability theory in their research.
Описание: Updated to conform to Mathematica® 7.0, this second edition shows how to easily create simulations from templates and solve problems using Mathematica. Along with new sections on order statistics, transformations of multivariate normal random variables, and Brownian motion, this edition offers an expanded section on Markov chains, more example data of the normal distribution, and more attention on conditional expectation. It also includes additional problems from Actuarial Exam P as well as new examples, exercises, and data sets. The accompanying CD-ROM contains updated Mathematica notebooks and a revised solutions manual is available for qualifying instructors.
Описание: Provides an introduction to probability theory and its applications.
Автор: Rao M.M., Swift R.J. Название: Probability Theory with Applications ISBN: 0387277307 ISBN-13(EAN): 9780387277301 Издательство: Springer Рейтинг: Цена: 12704 р. Наличие на складе: Поставка под заказ.
Описание: This book is a revised and expanded edition of a successful graduate and reference text. The material in the book is designed for a standard graduate course on probability theory, including some important applications. This new edition contains a detailed treatment of the core area of probability, and both structural and limit results are presented in full detail. Compared to the first edition, the material and presentation are better highlighted with several (small and large) alterations made to each chapter. Key features of the book include:
Описание: Contributed by world renowned researchers, the book features a wide range of important topics in modern statistical theory and methodology, economics and
finance, ecology, education, health and sports studies, and computer and IT-data mining. It is accessible to students and of interest to experts. Many of the contributions are concerned
with theoretical innovations, but all have applications in view, and some contain illustrations of the applied methods or photos of historic mathematicians.
A few of the notable
contributors are Ejaz Ahmed (Windsor), Joe Gani (ANU), Roger Gay (Monash), Atsuhiro Hayashi (NCUEE, Tokyo), Markus Hegland (ANU), Chris Heyde (ANU/Columbia), Jeff Hun
er (Massey), Phil Lewis (Canberra), Heinz Neudecker (Amsterdam), Graham Pollard (Canberra), Simo Puntanen (Tampere), George Styan (McGill), and Goetz Trenkler (Dortmund).
Описание: Probability and Statistics are as much about intuition and problem solving as they are about theorem proving. Because of this, students can find it very difficult to make a successful transition from lectures to examinations to practice, since the problems involved can vary so much in nature. Since the subject is critical in many modern applications such as mathematical finance, quantitative management, telecommunications, signal processing, bioinformatics, as well as traditional ones such as insurance, social science andengineering, the authors have rectified deficiencies in traditional lecture-based methods by collecting together a wealth of exercises with complete solutions, adapted to needs and skills of students. Following on from the success of Probability and Statistics by Example: Basic Probability and Statistics, the authors here concentrate on random processes, particularly Markov processes, emphasising modelsrather than general constructions. Basic mathematical facts are supplied as and when they are needed andhistorical information is sprinkled throughout.
Описание: Presents elementary probability theory with applications that illustrate the theory. This book reviews the basic elements of differential calculus which are used in the material to follow. It is suitable for students in pure and applied sciences such as mathematics, engineering, computer science, finance and economics.
Описание: A complete guide to the theory and practical applications of probability theory An Introduction to Probability Theory and Its Applications uniquely blends a comprehensive overview of probability theory with the real-world application of that theory.
Описание: This second edition of Probability With Statistical Applications offers a practical introduction to probability for undergraduates at all levels with different backgrounds and views towards applications. Calculus is a prerequisite for understanding the basic concepts, however the book is written with a sensitivity to students’ common difficulties with calculus that does not obscure the thorough treatment of the probability content. The first six chapters of this text neatly and concisely cover the material traditionally required by most undergraduate programs for a first course in probability.The comprehensive text includes a multitude of new examples and exercises, and careful revisions throughout. Particular attention is given to the expansion of the last three chapters of the book with the addition of two entirely new chapters on “Finding and Comparing Estimators” and “Multiple Linear Regression.” The classroom-tested material presented in this second edition textbook forms the basis for a second course introducing mathematical statistics.
Based on the author's course at NYU, Linear Algebra and Probability for Computer Science Applicationsgives an introduction to two mathematical fields that are fundamental in many areas of computer science. The course and the text are addressed to students with a very weak mathematical background. Most of the chapters discuss relevant MATLAB(R) functions and features and give sample assignments in MATLAB; the author's website provides the MATLAB code from the book.
After an introductory chapter on MATLAB, the text is divided into two sections. The section on linear algebra gives an introduction to the theory of vectors, matrices, and linear transformations over the reals. It includes an extensive discussion on Gaussian elimination, geometric applications, and change of basis. It also introduces the issues of numerical stability and round-off error, the discrete Fourier transform, and singular value decomposition. The section on probability presents an introduction to the basic theory of probability and numerical random variables; later chapters discuss Markov models, Monte Carlo methods, information theory, and basic statistical techniques. The focus throughout is on topics and examples that are particularly relevant to computer science applications; for example, there is an extensive discussion on the use of hidden Markov models for tagging text and a discussion of the Zipf (inverse power law) distribution.
Examples and Programming Assignments The examples and programming assignments focus on computer science applications. The applications covered are drawn from a range of computer science areas, including computer graphics, computer vision, robotics, natural language processing, web search, machine learning, statistical analysis, game playing, graph theory, scientific computing, decision theory, coding, cryptography, network analysis, data compression, and signal processing.
Homework Problems Comprehensive problem sections include traditional calculation exercises, thought problems such as proofs, and programming assignments that involve creating MATLAB functions.
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