This concise textbook, fashioned along the syllabus for master’s and Ph.D. programmes, covers basic results on discrete-time martingales and applications. It includes additional interesting and useful topics, providing the ability to move beyond. Adequate details are provided with exercises within the text and at the end of chapters. Basic results include Doob’s optional sampling theorem, Wald identities, Doob’s maximal inequality, upcrossing lemma, time-reversed martingales, a variety of convergence results and a limited discussion of the Burkholder inequalities. Applications include the 0-1 laws of Kolmogorov and Hewitt–Savage, the strong laws for U-statistics and exchangeable sequences, De Finetti’s theorem for exchangeable sequences and Kakutani’s theorem for product martingales. A simple central limit theorem for martingales is proven and applied to a basic urn model, the trace of a random matrix and Markov chains. Additional topics include forward martingale representation for U-statistics, conditional Borel–Cantelli lemma, Azuma–Hoeffding inequality, conditional three series theorem, strong law for martingales and the Kesten–Stigum theorem for a simple branching process. The prerequisite for this course is a first course in measure theoretic probability. The book recollects its essential concepts and results, mostly without proof, but full details have been provided for the Radon–Nikodym theorem and the concept of conditional expectation.

An essential guide to caring for your dog, filled with expert-backed tips and nuggets of advice to help every dog owner understand what their canine companion needs in order to be happy and healthy. In the Little Book of Dog Care, life-long dog lover and deathcare veterinary practice owner Ace Tilton Ratcliff delivers a must-have primer for every dog parent. What should you do when your dog is scared during a thunderstorm? How can you make clipping their nails less miserable? When do they like to eat? What can’t you feed them? Endless questions, expert-certified answers. Thoughtfully divided into chapters that focus on a specific aspect of care, from sleeping to grooming and beyond, these tips and tricks are applicable to any breed of dog. By the last page, every dog owner will better understand what their dog might be feeling—and how to best assist, using your enviable opposable thumbs.

Modern finance theory is vast and deep with various academic bases such as microeconomics, econometrics, probability theory, stochastic calculus, psychology, sociology, political economy, etc. depending on the specific research theme. Among those bases, this book is adopting probability theory and stochastic calculus to present some of the main contents of finance in a very concise manner. As a matter of fact, the objective of this book is to show, as concisely as possible, how probability and stochastic calculus is closely related to modern mathematical finance. So the organization of the book is to present theories of probability first and then their related financial theories later within each of the chapters in the theorem-proof style. From my past experience, students with a quantitative background prefer mathematical symbols to normal English sentences especially in case they are not native speakers of English. So I have tried to minimize the use of English sentences. This book is intended for upper level undergraduate courses and introductory graduate courses in mathematical finance for a single semester. This book can also be used for self-studying students with proper prerequisite knowledge. The only prerequisite for this book is one year courses of calculus.

The purpose of this book is to provide an overview of historical and recent results on concentration inequalities for sums of independent random variables and for martingales. The first chapter is devoted to classical asymptotic results in probability such as the strong law of large numbers and the central limit theorem. Our goal is to show that it is really interesting to make use of concentration inequalities for sums and martingales. The second chapter deals with classical concentration inequalities for sums of independent random variables such as the famous Hoeffding, Bennett, Bernstein and Talagrand inequalities. Further results and improvements are also provided such as the missing factors in those inequalities. The third chapter concerns concentration inequalities for martingales such as Azuma-Hoeffding, Freedman and De la Pena inequalities. Several extensions are also provided. The fourth chapter is devoted to applications of concentration inequalities in probability and statistics.

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Over the past eighty years, martingales have become central in the mathematics of randomness. They appear in the general theory of stochastic processes, in the algorithmic theory of randomness, and in some branches of mathematical statistics. Yet little has been written about the history of this evolution. This book explores some of the territory that the history of the concept of martingales has transformed. The historian of martingales faces an immense task. We can find traces of martingale thinking at the very beginning of probability theory, because this theory was related to gambling, and the evolution of a gambler’s holdings as a result of following a particular strategy can always be understood as a martingale. More recently, in the second half of the twentieth century, martingales became important in the theory of stochastic processes at the very same time that stochastic processes were becoming increasingly important in probability, statistics and more generally in various applied situations. Moreover, a history of martingales, like a history of any other branch of mathematics, must go far beyond an account of mathematical ideas and techniques. It must explore the context in which the evolution of ideas took place: the broader intellectual milieux of the actors, the networks that already existed or were created by the research, even the social and political conditions that favored or hampered the circulation and adoption of certain ideas. This books presents a stroll through this history, in part a guided tour, in part a random walk. First, historical studies on the period from 1920 to 1950 are presented, when martingales emerged as a distinct mathematical concept. Then insights on the period from 1950 into the 1980s are offered, when the concept showed its value in stochastic processes, mathematical statistics, algorithmic randomness and various applications.