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"At its simplest, Bayes's theorem describes the probability of an event, based on prior knowledge of conditions that might be related to the event. But Tom Chivers lays out how it affects every aspect of our lives. He explains why highly accurate screening tests can lead to false positives and how a failure to account for it in court has put innocent people in jail. But its influence goes far beyond practical applications. A cornerstone in rational...
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Drawing on his own groundbreaking work, Nate Silver examines the world of prediciton, investigating how we can distinguish a true signal from a universe of noisy data. Most predictions fail, often at great cost to society. But if our appreciation of uncertainty improves, our predicitons can get better. In keeping with his own aim to seek truth from data, Silver visits innovative forecasters in a range of fields, from hurricanes to baseball, from poker...
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The Mathematics of Games: An Introduction to Probability takes an inquiry-based approach to teaching the standard material for an introductory probability course. It also discusses different games and ideas that relate to the law of large numbers, as well as some more mathematical topics not typically found in similar books.
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The term probability can be used in two main senses. In the frequency interpretation it is a limiting ratio in a sequence of repeatable events. In the Bayesian view, probability is a mental construct representing uncertainty. This book is about these two types of probability and investigates how, despite being adopted by scientists and statisticians in the eighteenth and nineteenth centuries, Bayesianism was discredited as a theory of scientific inference...
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There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most introductory statistics texts only present frequentist methods. In Bayesian statistics the rules of probability are used to make inferences about the parameter. Prior information about the parameter and sample information from the data are combined using Bayes theorem. Bayesian statistics has many important advantages that students should learn about...
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The product of years of research and practical experience in pattern classification, this book offers a theory-based engineering perspective on neural networks and statistical pattern classification. Pattern Classification sheds new light on the relationship between seemingly unrelated approaches to pattern recognition, including statistical methods, polynomial regression, multilayer perceptron, and radial basis functions. Important topics such as...
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"Forty years ago, Israeli psychologists Daniel Kahneman and Amos Tversky wrote a series of studies undoing our assumptions about the decision-making process. Their papers showed the ways in which the human mind erred, systematically, when forced to make judgments in uncertain situations. Their work created the field of behavioral economics, revolutionized Big Data studies, advanced evidence-based medicine, led to a new approach to government regulation,...
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