Flip S Fair

This classroom-tested textbook is an introduction to probability theory, with the right balance between mathematical precision, probabilistic intuition, and concrete applications. Introduction to Probability covers the material precisely, while avoiding excessive technical details. After introducing the basic vocabulary of randomness, including events, probabilities, and random variables, the text offers the reader a first glimpse of the major theorems of the subject: the law of large numbers and the central limit theorem. The important probability distributions are introduced organically as they arise from applications. The discrete and continuous sides of probability are treated together to emphasize their similarities. Intended for students with a calculus background, the text teaches not only the nuts and bolts of probability theory and how to solve specific problems, but also why the methods of solution work.

Epraim is horrified when he comes home from school one day to find his mother unconscious at the kitchen table, clutching a bottle of pills. Even more disturbing than her suicide attempt is the reason for it: the dead boy she identified at the hospital that afternoon--a boy who looks exactly like him. While examining his dead double's belongings, Ephraim discovers a strange coin that makes his wishes come true each time he flips it. Before long, he's wished his alcoholic mother into a model parent, and the girl he's liked since second grade suddenly notices him. But Ephraim soon realizes that the coin comes with consequences --several wishes go disastrously wrong, his best friend Nathan becomes obsessed with the coin, and the world begins to change in unexpected ways. As Ephraim learns the coin's secrets and how to control its power, he must find a way to keep it from Nathan and return to the world he remembers. (For ages 12 & up) From the Hardcover edition.

Bayesian ideas have recently been applied across such diverse fields as philosophy, statistics, economics, psychology, artificial intelligence, and legal theory. Fundamentals of Bayesian Epistemology examines epistemologists' use of Bayesian probability mathematics to represent degrees of belief. Michael G. Titelbaum provides an accessible introduction to the key concepts and principles of the Bayesian formalism, enabling the reader both to follow epistemological debates and to see broader implications Volume 1 begins by motivating the use of degrees of belief in epistemology. It then introduces, explains, and applies the five core Bayesian normative rules: Kolmogorov's three probability axioms, the Ratio Formula for conditional degrees of belief, and Conditionalization for updating attitudes over time. Finally, it discusses further normative rules (such as the Principal Principle, or indifference principles) that have been proposed to supplement or replace the core five. Volume 2 gives arguments for the five core rules introduced in Volume 1, then considers challenges to Bayesian epistemology. It begins by detailing Bayesianism's successful applications to confirmation and decision theory. Then it describes three types of arguments for Bayesian rules, based on representation theorems, Dutch Books, and accuracy measures. Finally, it takes on objections to the Bayesian approach and alternative formalisms, including the statistical approaches of frequentism and likelihoodism.

Developed from celebrated Harvard statistics lectures, Introduction to Probability provides essential language and tools for understanding statistics, randomness, and uncertainty. The book explores a wide variety of applications and examples, ranging from coincidences and paradoxes to Google PageRank and Markov chain Monte Carlo (MCMC). Additional application areas explored include genetics, medicine, computer science, and information theory. The print book version includes a code that provides free access to an eBook version. The authors present the material in an accessible style and motivate concepts using real-world examples. Throughout, they use stories to uncover connections between the fundamental distributions in statistics and conditioning to reduce complicated problems to manageable pieces. The book includes many intuitive explanations, diagrams, and practice problems. Each chapter ends with a section showing how to perform relevant simulations and calculations in R, a free statistical software environment.

While competitive natural selection is widely assumed to be evolution’s prime mover, Weiss shows how life generally works on the basis of cooperation. He reveals that focus on competition and cooperation is largely an artifact of compression of time—a distortion that dissolves when life is viewed from developmental and evolutionary time scales.

A nontechnical guide to the basic ideas of modern causal inference, with illustrations from health, the economy, and public policy. Which of two antiviral drugs does the most to save people infected with Ebola virus? Does a daily glass of wine prolong or shorten life? Does winning the lottery make you more or less likely to go bankrupt? How do you identify genes that cause disease? Do unions raise wages? Do some antibiotics have lethal side effects? Does the Earned Income Tax Credit help people enter the workforce? Causal Inference provides a brief and nontechnical introduction to randomized experiments, propensity scores, natural experiments, instrumental variables, sensitivity analysis, and quasi-experimental devices. Ideas are illustrated with examples from medicine, epidemiology, economics and business, the social sciences, and public policy.

The coin toss is really just a metaphor for a random event that has only two possible outcomes. The actual tossing of a real coin is just one way to realize such an event. There are many examples of questions that are equivalent to a coin toss. For example: Will the stock market close up or down tomorrow? Will a die roll come up with an even or odd number? Will we make contact with extraterrestrials within the next ten years? Will a car drive by in the next minute? Will tomorrow be sunny or cloudy? Will my medical test result be negative or positive? Will I enjoy this movie? Will the next joke be funny? Will the Earth's average temperature go up next year?Because a coin toss is equivalent to such a wide variety of questions, the results in this book are widely applicable.Because the coin toss is the simplest random event you can imagine, many questions about coin tossing can be asked and answered in great depth. The simplicity of the coin toss also opens the road to more advanced probability theories dealing with events with an infinite number of possible outcomes.This book is very mathematical. Some knowledge of calculus, discrete math, and generating functions is helpful to get the most out of it. A review of discrete math is provided in the index,

With its engaging and conversational tone, Essential Biostatistics: A Nonmathematical Approach provides a clear introduction to statistics for students in a wide range of fields, and a concise statistics refresher for scientists and professionals who need to interpret statistical results. It explains the ideas behind statistics in nonmathematical terms, offers perspectives on how to interpret published statistical results, and points out common conceptual traps to avoid. It can be used as a stand-alone text or as a supplement to a traditional statistics textbook.