Bayesian Statistics is the school of thought that combines prior beliefs with the likelihood of a hypothesis to arrive at posterior beliefs. The first edition of Peter Lee’s book appeared in 1989, but the subject has moved ever onwards, with increasing emphasis on Monte Carlo based techniques.This new fourth edition looks at recent techniques such as variational methods, Bayesian importance sampling, approximate Bayesian computation and Reversible Jump Markov Chain Monte Carlo (RJM...

Now that people are aware that data can make the difference in an election or a business model, data science as an occupation is gaining ground. But how can you get started working in a wide-ranging, interdisciplinary field that’s so clouded in hype? This insightful book, based on Columbia University’s Introduction to Data Science class, tells you what you need to know.In many of these chapter-long lectures, data scientists from companies such as Google, Microsoft, and eBay share n...

An Introduction to Stochastic Modeling, Student Solutions Manual (e-only)

Chance and Luck: The Laws of Luck, Coincidences, Wagers, Lotteries, and the Fallacies of Gambling The false ideas prevalent among all classes of the community, cultured as well as uncultured, respecting chance and luck, illustrate the truth that common consent (in matters outside the influence of authority) argues almost of necessity error. This, by the way, might be proved by the method of probabilities. For if, in any question of difficulty, the chance that an ...

So much to read, so little time? This brief overview of The Signal and the Noise tells you what you need to know—before or after you read Nate Silver's book.Crafted and edited with care, Worth Books set the standard for quality and give you the tools you need to be a well-informed reader.This short summary and analysis of The Signal and the Noise by Nate Silver includes:Historical contextChapter-by-chapter summaries

Bayesian Networks: An Introduction provides a self-contained introduction to the theory and applications of Bayesian networks, a topic of interest and importance for statisticians, computer scientists and those involved in modelling complex data sets. The material has been extensively tested in classroom teaching and assumes a basic knowledge of probability, statistics and mathematics. All notions are carefully explained and feature exercises throughout.Features include:

In this terrible new COVID-19 world, the University of Ottawa is doing its part by offering a 50% discount on this very important book. We decided not to rewrite the witty book description, though we realize it is tone-deaf at the present moment, as we wanted to give readers a sense of the tone of this title. But don’t be deceived: while a fun read, this book will help you better understand how epidemiologists, governments and health care planners use mathematical models to figure out ...

In the evaluation of healthcare, rigorous methods of quantitative assessment are necessary to establish interventions that are both effective and cost-effective. Usually a single study will not fully address these issues and it is desirable to synthesize evidence from multiple sources. This book aims to provide a practical guide to evidence synthesis for the purpose of decision making, starting with a simple single parameter model, where all studies estimate the same quantity (pairwise met...

This textbook systematically presents fundamental methods of statistical analysis: from probability and statistical distributions, through basic concepts of statistical inference, to a collection of methods of analysis useful for scientific research. It is rich in tables, diagrams, and examples, in addition to theoretical justification of the methods of analysis introduced. Each chapter has a section entitled “Exercises and Problems” to accompany the text. There are altogether about 300 ex...

Praise for the First Edition"...a reference for everyone who is interested in knowing and handling uncertainty."—Journal of Applied StatisticsThe critically acclaimed First Edition of Understanding Uncertainty provided a study of uncertainty addressed to scholars in all fields, showing that uncertainty could be measured by probability, and that probability obeyed three basic rules that enabled uncertainty t...

Covers basic statistics and applications of major statistical tests. Preents the material in a sequence and approach that can be understood and used by almost anyone. Although this can be(and is) used as a text it is equally as useful as a study-aid for use with courses using more conventional texts. It is especially useful for those frightened by math or who are otherwise not well-prepared mathematically. It was developed as a text for Bachelor and Masters level students who have previous...

The author's research has been directed towards inference involving observables rather than parameters. In this book, he brings together his views on predictive or observable inference and its advantages over parametric inference. While the book discusses a variety of approaches to prediction including those based on parametric, nonparametric, and nonstochastic statistical models, it is devoted mainly to predictive applications of the Bayesian approach. It not only substitutes predictive a...