**Vidyadhar S. Mandrekar, David A. Redett**

November 06, 2017

The first book to examine weakly stationary random fields and their connections with invariant subspaces (an area associated with functional analysis). It reviews current literature, presents central issues and most important results within the area. For advanced Ph.D. students,...

**Siva Athreya, V. S. Sunder**

October 31, 2017

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,...

**Peter Watts Jones, Peter Smith**

October 16, 2017

Based on a well-established and popular course taught by the authors over many years, Stochastic Processes: An Introduction, Third Edition, discusses the modelling and analysis of random experiments, where processes evolve over time. The text begins with a review of relevant fundamental probability...

**Han-Fu Chen, Wenxiao Zhao**

October 12, 2017

Recursive Identification and Parameter Estimation describes a recursive approach to solving system identification and parameter estimation problems arising from diverse areas. Supplying rigorous theoretical analysis, it presents the material and proposed algorithms in a manner that makes it easy to...

**Peter Watts Jones**

July 27, 2017

Based on a highly popular, well-established course taught by the authors, Stochastic Processes: An Introduction, Second Edition discusses the modeling and analysis of random experiments using the theory of probability. It focuses on the way in which the results or outcomes of experiments vary and...

**Peter J. Veazie**

May 03, 2017

What Makes Variables Random: Probability for the Applied Researcher provides an introduction to the foundations of probability that underlie the statistical analyses used in applied research. By explaining probability in terms of measure theory, it gives the applied researchers a conceptual...

**Mark P. Kaminskiy**

March 29, 2017

A discussion of the basic reliability concepts and models, Reliability Models for Engineers and Scientists demystifies modern mathematical reliability models. Requiring very little mathematical background on the reader’s part, this concise book introduces the models by focusing on their physical...

**Boris Brodsky**

March 23, 2017

This book covers the development of methods for detection and estimation of changes in complex systems. These systems are generally described by nonstationary stochastic models, which comprise both static and dynamic regimes, linear and nonlinear dynamics, and constant and time-variant structures...

**Pierre Del Moral, Spiridon Penev**

December 19, 2016

Unlike traditional books presenting stochastic processes in an academic way, this book includes concrete applications that students will find interesting such as gambling, finance, physics, signal processing, statistics, fractals, and biology. Written with an important illustrated guide in the...

**Larry Rabinowitz**

December 13, 2016

Elementary Probability with Applications, Second Edition shows students how probability has practical uses in many different fields, such as business, politics, and sports. In the book, students learn about probability concepts from real-world examples rather than theory. The text explains how...

**Mohammad Modarres, Mark P. Kaminskiy, Vasiliy Krivtsov**

December 01, 2016

This undergraduate and graduate textbook provides a practical and comprehensive overview of reliability and risk analysis techniques. Written for engineering students and practicing engineers, the book is multi-disciplinary in scope. The new edition has new topics in classical confidence interval...

**Pierre Del Moral**

October 26, 2016

In the last three decades, there has been a dramatic increase in the use of interacting particle methods as a powerful tool in real-world applications of Monte Carlo simulation in computational physics, population biology, computer sciences, and statistical machine learning. Ideally suited to...