Originating from the authors' own graduate course at the University of North Carolina, this material has been thoroughly tried and tested over many years, making the book perfect for a two-term course or for self-study. It provides a concise introduction that covers all of the measure theory and probability most useful for statisticians, including Lebesgue integration, limit theorems in probability, martingales, and some theory of stochastic processes. Readers can test their understanding of the material through the 300 exercises provided. The book is especially useful for graduate students in statistics and related fields of application (biostatistics, econometrics, finance, meteorology, machine learning, and so on) who want to shore up their mathematical foundation. The authors establish common ground for students of varied interests which will serve as a firm 'take-off point' for them as they specialize in areas that exploit mathematical machinery.

On behalf of those of us who in various ways have con tributed to this volume, and on behalf of all of his colleagues, students and friends throughout the world-wide scientific com munity, we dedicate this volume to Gopinath Kallianpur as a tribute to his work and in appreciation for the insights which he has so graciously and generously offered, and continues to offer, to all of us. Stochastic Processes contains 41 articles related to and frequently influ enced by Kallianpur's work. We regret that space considerations prevented us from including contributions from his numerous colleagues (at North Carolina, lSI, Minnesota, Michigan), former students, co-authors and other eminent scientists whose work is akin to Kallianpur's. This would have taken several more volumes All articles have been refereed, and for their valuable assistance in this we thank many of the contributing authors, as well as: R. Bradley, M.H.A. Davis, R. Davis, J. Hawkins, J. Horowitz, C. Houdre, N.C. Jain, C. Ji, P. Kokoszka, T. Kurtz, K.S. Lau, W. Linde, D. Monrad, D. Stroook, D. Surgailis and S. Yakowitz. We also thank June Maxwell for editorial assistance, Peggy Ravitch for help with the production of the volume, and Lisa Brooks for secretarial assistance. Finally, we are indebted to Dr. Martin Gilchrist, the Statistics editor, and the Springer editorial board for their excellent cooperation and enthusiastic support throughout this project."

Originating from the authors' own graduate course at the University of North Carolina, this material has been thoroughly tried and tested over many years, making the book perfect for a two-term course or for self-study. It provides a concise introduction that covers all of the measure theory and probability most useful for statisticians, including Lebesgue integration, limit theorems in probability, martingales, and some theory of stochastic processes. Readers can test their understanding of the material through the 300 exercises provided. The book is especially useful for graduate students in statistics and related fields of application (biostatistics, econometrics, finance, meteorology, machine learning, and so on) who want to shore up their mathematical foundation. The authors establish common ground for students of varied interests which will serve as a firm 'take-off point' for them as they specialize in areas that exploit mathematical machinery.