Arising from courses taught by the authors, this largely self-contained treatment is ideal for mathematicians who are interested in applications or for students from applied fields who want to understand the mathematics behind their subject. Early chapters cover Fourier analysis, functional analysis, probability and linear algebra, all of...

"This interesting monograph is devoted to the study of multi-agent, populations of biological cells and robot teams. … The main objective of the approach is to provide solutions to the problem of how dynamics of an individual agent propagates to the population dynamics. … This original monograph has a wide...

This is a textbook for a one-semester graduate course in measure-theoretic probability theory, but with ample material to cover an ordinary year-long course at a more leisurely pace. Khoshnevisan's approach is to develop the ideas that are absolutely central to modern probability theory, and to showcase them by presenting their various...

Modern astronomical research is beset with a vast range of statistical challenges, ranging from reducing data from megadatasets to characterizing an amazing variety of variable celestial objects or testing astrophysical theory. Linking astronomy to the world of modern statistics, this volume is a unique resource, introducing astronomers to...

Perhaps the largest impact that Bayesian networks have had is on the field of AI, where they were...

This book provides an introduction to discrete-time and discrete-frequency signal processing, which is rapidly becoming an important, modern way to design and analyze electronics projects of all...

A First Course in Probability, Eighth Edition, features clear and intuitive explanations of the mathematics of probability theory, outstanding problem sets, and a variety of diverse examples and applications. This book is ideal for an upper-level undergraduate or graduate level introduction to probability for math, science, engineering...