This book is based on the view that cognitive skills are best acquired by solving challenging, non-standard probability problems. Many puzzles and problems presented here are either new within a problem solving context (although as topics in fundamental research they are long known) or are variations of classical problems which follow directly from elementary concepts. A small number of particularly instructive problems is taken from previous sources which in this case are generally given. This book will be a handy resource for professors looking for problems to assign, for undergraduate math students, and for a more general audience of amateur scientists.

Probability and Mathematical Statistics: An Introduction provides a well-balanced first introduction to probability theory and mathematical statistics. This book is organized into two sections encompassing nine chapters. The first part deals with the concept and elementary properties of probability space, and random variables and their probability distributions. This part also considers the principles of limit theorems, the distribution of random variables, and the so-called student’s distribution. The second part explores pertinent topics in mathematical statistics, including the concept of sampling, estimation, and hypotheses testing. This book is intended primarily for undergraduate statistics students.

Approximately 1,000 problems — with answers and solutions included at the back of the book — illustrate such topics as random events, random variables, limit theorems, Markov processes, and much more.

One of the hardest questions that mathematics teachers have to answer is "Why?" Schoolroom sums are crucial in learning the awesome power of mathematics, but they are often a world away from how the knowledge is applied and where it came from. Inside Mathematics: Probability & Statistics is there to fill that gap. What are the chances of that? Mathematics can solve that mystery for you using a set of ideas that grew out of an aristocratic gambler's bafflement at betting on complex dice games. In stepped the mathematical giants of Pierre de Fermat and Blaise Pascal, who worked together to create what is now called probability theory. Gamblers need not rejoice in this powerful theory; it shows that the casino always wins in the end. The ideas of probability have since found many better uses elsewhere. For example, they are at work in the mathematics that describes the quantum world and drives the push for artificial intelligence. The mathematics of chance is involved in understanding systems where a myriad data points combine. Statistics is the branch of mathematics that wrangles that data and tames it into meaningful knowledge. It then allows us to get ever better at modeling complex phenomena, from the formation of stars and the path of a hurricane to the rise and fall of the markets. Inside Mathematics: Probability & Statistics introduces the reader to these awesome mathematical powers by telling the stories of who figured them out. They include a cavalry officer hoping to reduce injuries from horse kicks, Charles Darwin's cousin who discovered that we make the best guesses when we work together, and computers that are built to program themselves. Written to engage and enthuse young people, Inside Mathematics shows readers how the ideas of long-dead geniuses have ended up in their homework assignments. Probability & Statistics: How Mathematics Can Predict the Future changes the question from "Why?" to "What's next?" Arranged chronologically to show how ideas in mathematics evolved.

This book of problems is designed to challenge students learning probability. Each chapter is divided into three parts: Problems, Hints, and Solutions. All Problems sections include expository material, making the book self-contained. Definitions and statements of important results are interlaced with relevant problems. The only prerequisite is basic algebra and calculus.

Provides the necessary skills to solve problems in mathematical statistics through theory, concrete examples, and exercises With a clear and detailed approach to the fundamentals of statistical theory, Examples and Problems in Mathematical Statistics uniquely bridges the gap between theory andapplication and presents numerous problem-solving examples that illustrate the relatednotations and proven results. Written by an established authority in probability and mathematical statistics, each chapter begins with a theoretical presentation to introduce both the topic and the important results in an effort to aid in overall comprehension. Examples are then provided, followed by problems, and finally, solutions to some of the earlier problems. In addition, Examples and Problems in Mathematical Statistics features: Over 160 practical and interesting real-world examples from a variety of fields including engineering, mathematics, and statistics to help readers become proficient in theoretical problem solving More than 430 unique exercises with select solutions Key statistical inference topics, such as probability theory, statistical distributions, sufficient statistics, information in samples, testing statistical hypotheses, statistical estimation, confidence and tolerance intervals, large sample theory, and Bayesian analysis Recommended for graduate-level courses in probability and statistical inference, Examples and Problems in Mathematical Statistics is also an ideal reference for applied statisticians and researchers.