The late Professor Pao-Lu Hsu's statistical work was primarily concerned with inference in univariate and multivariate linear models and with the associated distribution theory, both exact and asymptotic. This volume contains all of Hsu's mathematical papers published between 1935 and 1970. It comprises 40 articles, and several additional commentaries and discussions of Hsu's work in inference, multivariate analysis, and probability by Lehmann, Anderson, and Chung. Further commentaries, by various authors, have been appended to some of the papers. Hsu's colleagues Kiang and Tuan in Beijing have rewritten their memorial tribute for this occasion.

From the reviews:

J. Neveu, 1962 in Zentralblatt fur Mathematik, 92.Band Heft 2, p. 343: "Ce livre ecrit par l'un des plus eminents specialistes en la matiere, est un expose tres detaille de la theorie des processus de Markov definis sur un espace denombrable d'etats et homogenes dans le temps (chaines stationnaires de Markov)."

N.Jain, 2008 in Selected Works of Kai Lai Chung, edited by Farid AitSahlia (University of Florida, USA), Elton Hsu (Northwestern University, USA), & Ruth Williams (University of California-San Diego, USA), Chapter 1, p. 15: "This monograph deals with countable state Markov chains in both discrete time (Part I) and continuous time (Part II). ...] Much of Kai Lai's fundamental work in the field is included in this monograph. Here, for the first time, Kai Lai gave a systematic exposition of the subject which includes classification of states, ratio ergodic theorems, and limit theorems for functionals of the chain."

"

This book provides an introduction to probability theory and its applications. The emphasis is on essential probabilistic reasoning, which is illustrated with a large number of samples. The fourth edition adds material related to mathematical finance as well as expansions on stable laws and martingales.

From the reviews: "Almost thirty years after its first edition, this charming book continues to be an excellent text for teaching and for self study." -- STATISTICAL PAPERS

From the reviews of the First Edition:

"This excellent book is based on several sets of lecture notes written over a decade and has its origin in a one-semester course given by the author at the ETH, Zurich, in the spring of 1970. The author's aim was to present some of the best features of Markov processes and, in particular, of Brownian motion with a minimum of prerequisites and technicalities. The reader who becomes acquainted with the volume cannot but agree with the reviewer that the author was very successful in accomplishing this goal...The volume is very useful for people who wish to learn Markov processes but it seems to the reviewer that it is also of great interest to specialists in this area who could derive much stimulus from it. One can be convinced that it will receive wide circulation." (Mathematical Reviews)

This new edition contains 9 new chapters which include new exercises, references, and multiple corrections throughout the original text."

This is a collection of articles by Kai Lai Chung, previously published in the series S minaire de Probabilit?'s of the Lecture Notes in Mathematics, published on the occasion of the 2010 conference in Hong Kong in memory of Kai Lai Chung.

This is an introductory textbook on probability theory and its applications. Basic concepts such as probability measure, random variable, distribution, and expectation are fully treated without technical complications. Both the discrete and continuous cases are covered, the elements of calculus being used in the latter case. The emphasis is on essential probabilistic reasoning, amply motivated, explained, and illustrated with a large number of carefully selected examples. Special topics include combinatorial problems, urn schemes, Poisson processes, random walks, genetic models, and Markov chains. Problems with solutions are provided at the end of each chapter. Its easy style and full discussion make this a useful text not only for mathematics and statistics majors, but also for students in engineering and physical, biological, and social sciences. This edition adds two new chapters covering applications to mathematical finance. Elements of modern portfolio and option theories are presented in a detailed and rigorous manner. The approach distinguishes this text from other more mathematically advanced treatises or more technical manuals. Kai Lai Chung is Professor Emeritus of Mathematics at Stanford University. Farid AitSahlia is a Senior Scientist with DemandTec, where he develops econometric and optimization methods for demand-based pricing models. He is also a visiting scholar in the department of statistics at Stanford University, where he obtained his Ph.D.in operations research.

From the reviews of the First Edition:

"This excellent book is based on several sets of lecture notes written over a decade and has its origin in a one-semester course given by the author at the ETH, Zurich, in the spring of 1970. The author's aim was to present some of the best features of Markov processes and, in particular, of Brownian motion with a minimum of prerequisites and technicalities. The reader who becomes acquainted with the volume cannot but agree with the reviewer that the author was very successful in accomplishing this goal...The volume is very useful for people who wish to learn Markov processes but it seems to the reviewer that it is also of great interest to specialists in this area who could derive much stimulus from it. One can be convinced that it will receive wide circulation." (Mathematical Reviews)

This new edition contains 9 new chapters which include new exercises, references, and multiple corrections throughout the original text."

From the reviews:

J. Neveu, 1962 in Zentralblatt fur Mathematik, 92.Band Heft 2, p. 343: "Ce livre ecrit par l'un des plus eminents specialistes en la matiere, est un expose tres detaille de la theorie des processus de Markov definis sur un espace denombrable d'etats et homogenes dans le temps (chaines stationnaires de Markov)."

N.Jain, 2008 in Selected Works of Kai Lai Chung, edited by Farid AitSahlia (University of Florida, USA), Elton Hsu (Northwestern University, USA), & Ruth Williams (University of California-San Diego, USA), Chapter 1, p. 15: "This monograph deals with countable state Markov chains in both discrete time (Part I) and continuous time (Part II). ...] Much of Kai Lai's fundamental work in the field is included in this monograph. Here, for the first time, Kai Lai gave a systematic exposition of the subject which includes classification of states, ratio ergodic theorems, and limit theorems for functionals of the chain."

"

The theory of Markov chains, although a special case of Markov processes, is here developed for its own sake and presented on its own merits. In general, the hypothesis of a denumerable state space, which is the defining hypothesis of what we call a "chain" here, generates more clear-cut questions and demands more precise and definitive an swers. For example, the principal limit theorem ( 1. 6, II. 10), still the object of research for general Markov processes, is here in its neat final form; and the strong Markov property ( 11. 9) is here always applicable. While probability theory has advanced far enough that a degree of sophistication is needed even in the limited context of this book, it is still possible here to keep the proportion of definitions to theorems relatively low. . From the standpoint of the general theory of stochastic processes, a continuous parameter Markov chain appears to be the first essentially discontinuous process that has been studied in some detail. It is common that the sample functions of such a chain have discontinuities worse than jumps, and these baser discontinuities play a central role in the theory, of which the mystery remains to be completely unraveled. In this connection the basic concepts of separability and measurability, which are usually applied only at an early stage of the discussion to establish a certain smoothness of the sample functions, are here applied constantly as indispensable tools.

Since the publication of the first edition of this classic textbook over thirty years ago, tens of thousands of students have used A Course in Probability Theory. New in this edition is an introduction to measure theory that expands the market, as this treatment is more consistent with current courses.
While there are several books on probability, Chung's book is considered a classic, original work in probability theory due to its elite level of sophistication.