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UNCERTAINTY: THE SOUL OF MODELING, PROBABILITY & By William Briggs - Hardcover - Picture 1 of 1
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UNCERTAINTY: THE SOUL OF MODELING, PROBABILITY & By William Briggs - Hardcover

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Condition:
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Like New
A book that has been read, but looks new. The book cover has no visible wear, and the dust jacket (if applicable) is included for hard covers. No missing or damaged pages, no creases or tears, no underlining or highlighting of text, and no writing in the margins. May have no identifying marks on the inside cover. No wear and tear. See the seller’s listing for full details and description of any imperfections. See all condition definitionsopens in a new window or tab
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“Book is in Like New / near Mint Condition. Will include dust jacket if it originally came with ...
ISBN-10
3319397559
Book Title
Uncertainty: The Soul of Modeling, Probability & Statistics
Item Height
9.21 inches
ISBN
9783319397559

About this product

Product Identifiers

Publisher
Springer International Publishing A&G
ISBN-10
3319397559
ISBN-13
9783319397559
eBay Product ID (ePID)
224018237

Product Key Features

Number of Pages
Xix, 258 Pages
Publication Name
Uncertainty : the Soul of Modeling, Probability and Statistics
Language
English
Publication Year
2016
Subject
History & Philosophy, Epistemology, Probability & Statistics / General, General
Type
Textbook
Subject Area
Mathematics, Philosophy, Psychology
Author
William Briggs
Format
Hardcover

Dimensions

Item Weight
192 Oz
Item Length
9.3 in
Item Width
6.1 in

Additional Product Features

Reviews
"Briggs, an adjunct professor of statistics at Cornell University, cautions his readers to carefully examine the uncertain reliability of such conclusions when these tools are used. His challenging premises are thoroughly supported by philosophical explanations as to why these traditional approaches need to be questioned. ... Briggs provides fully fleshed out reasoning, impressive support, precisely worded insight, and graphical illustrations, as appropriate, to justify his stand. ... Summing Up: Recommended. Upper-division undergraduates and above; faculty and professionals." (N. W. Schillow, Choice, Vol. 54 (6), February, 2017) "[This book] is not for sissies, true, but its clear-headed (i.e., Aristotelian) approach to the subject of truth (which, in the end, is what exercises in probability and statistical analysis are all about, notwithstanding what they tell you in school) is refreshing: a long, cool drink of plain speaking about intellectual topics that, in these hot and humid days, is as enlivening as it is enlightening." (Roger Kimball, The New Criterion's Critic's Notebook, newcriterion.com, August, 2016) "This book has the potential to turn the world of evidence-based medicine upside down. It boldly asserts that with regard to everything having to do with evidence, we're doing it all wrong: probability, statistics, causality, modeling, deciding, communicating--everything. ... the book is full of humor and a delight to read and re-read." (Jane M. Orient, Journal of American Physicians and Surgeons, Vol. 21 (3), 2016), "Briggs, an adjunct professor of statistics at Cornell University, cautions his readers to carefully examine the uncertain reliability of such conclusions when these tools are used. His challenging premises are thoroughly supported by philosophical explanations as to why these traditional approaches need to be questioned. ... Briggs provides fully fleshed out reasoning, impressive support, precisely worded insight, and graphical illustrations, as appropriate, to justify his stand. ... Summing Up: Recommended. Upper-division undergraduates and above; faculty and professionals." (N. W. Schillow, Choice, Vol. 54 (6), February, 2017) "This is a book about probability and probabilistic reasoning. It is more philosophy than mathematics, but it does have mathematical content and it relies in some measure on mathematical reasoning. ... This book is worth a look by anyone who teaches probability and statistics." (William J. Satzer, MAA Reviews, August, 2016) "[This book] is not for sissies, true, but its clear-headed (i.e., Aristotelian) approach to the subject of truth (which, in the end, is what exercises in probability and statistical analysis are all about, notwithstanding what they tell you in school) is refreshing: a long, cool drink of plain speaking about intellectual topics that, in these hot and humid days, is as enlivening as it is enlightening." (Roger Kimball, The New Criterion's Critic's Notebook, newcriterion.com, August, 2016) "This book has the potential to turn the world of evidence-based medicine upside down. It boldly asserts that with regard to everything having to do with evidence, we're doing it all wrong: probability, statistics, causality, modeling, deciding, communicating--everything. ... the book is full of humor and a delight to read and re-read." (Jane M. Orient, Journal of American Physicians and Surgeons, Vol. 21 (3), 2016)
Number of Volumes
1 vol.
Illustrated
Yes
Table Of Content
Truth, Argument, Realism.- Logic.- Induction and Intellection.- What Probability Is.- What Probability Is Not.- Chance and Randomness.- Causality.- Probability Models.- Statistical and Physical Models.- Modelling Goals, Strategies, and Mistakes.
Synopsis
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.
LC Classification Number
QA276-280

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