Uncertainty: The Soul of Modeling, Probability & Statistics

by William Briggs

This book presents a philosophical approach to probability and probabilistic thinking, considering the underpinnings of probabilistic reasoning and modeling, which effectively underlie everything in data science. The ultimate goal is to call into question many standard tenets and lay the philosophical and probabilistic groundwork and infrastructure for statistical modeling. It is the first book devoted to the philosophy of data aimed at working scientists and calls for a new consideration in the practice of probability and statistics to eliminate what has been referred to as the "Cult of Statistical Significance."  The book explains the philosophy of these ideas and not the mathematics, though there are a handful of mathematical examples. The topics are logically laid out, starting with basic philosophy as related to probability, statistics, and science, and stepping through the key probabilistic ideas and concepts, and ending with statistical models.  Its jargon-free approach asserts that standard methods, such as out-of-the-box regression, cannot help in discovering cause. This new way of looking at uncertainty ties together disparate fields — probability, physics, biology, the “soft” sciences, computer science — because each aims at discovering cause (of effects). It broadens the understanding beyond frequentist and Bayesian methods to propose a Third Way of modeling.

Genres: Philosophy, Mathematics, Science, Reference

277 pages, Kindle Edition

Published July 15, 2016

About the author

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Reviews

[Paul, · Rating 3 out of 5]

As a PhD student in Economics, I think this book is important! I think I agree with its main argument. But, the writing style leaves me wrestling with how to express these ideas to scholars in my field. The author is making a point with his use of notation, but that also makes comparison difficult, at least for non-experts like myself.

The formatting of the Kindle edition is terrible.