Uncertainty

William , Briggs


anglais | 30-05-2018 | 280 pages

9783319819587

Livre de poche


95,49€

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Couverture / Jaquette

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.

Note biographique

William M. Briggs, PhD, is Adjunct Professor of Statistics at Cornell University.  Having earned both his PhD in Statistics and MSc in Atmospheric Physics from Cornell University, he served as the editor of the American Meteorological Society journal and has published over 60 papers.  He studies the philosophy of science, the use and misuses of uncertainty - from truth to modeling.  Early in life, he began his career as a cryptologist for the Air Force, then slipped into weather and climate forecasting, and later matured into an epistemologist.  Currently, he has a popular, long-running blog on the subjects written about here, with about 70,000 - 90,000 monthly readers.


Fonctionnalité

Presents a complete argument showing why probability should be treated as a part of logic

Broadens understanding beyond frequentist and Bayesian methods, proposing a Third Way of modeling

Proposes that p-values should die, and along with them, hypothesis testing

Table des matières

1.  Truth, Argument, Realism1.1. Truth1.2. Realism1.3. Epistemology1.4. Necessary & Conditional Truth1.5. Science & Scientism1.6. Faith1.7. Belief & Knowlege2.  Logic2.1. Language2.2. Logic Is Not Empirical2.3. Syllogistic Logic2.4. Syllogisms2.5. Informality2.6. Fallacy3.  Induction and Intellection3.1. Metaphysics3.2. Types of Induction3.3. Grue4.  What Probability Is4.1. Probability Is Conditional4.2. Relevance 4.3. The Proportional Syllogism4.4. Details4.5. Assigning Probability4.6. Weight of Probability4.7. Probability Usually Is Not a Number4.8. Probability Can Be a Number5.  What Probability Is Not5.1. Probability Is Not Physical5.2. Probability & Essence5.3. Probability Is Not Subjective5.4. Probability Is Not Only Relative Frequency5.5. Probability Is Not Always a Number Redux6.  Chance and Randomness6.1. Randomness6.2. Not a Cause6.3. Experimental Design & Randomization6.4. Nothing Is Distributed6.5. Quantum Mechanics6.6. Simulations6.7. Truly Random & Information Theory7.  Causality7.1. What Is Cause Like?7.2. Causal Models7.3. Paths 7.4. Once a Cause, Always a Cause7.5. Falsifiability7.6. Explanation7.7. Under-Determination8.  Probability Models8.1. Model Form8.2. Relevance & Importance8.3. Independence versus Irrelevance8.4. Bayes8.5. The Problem and Origin of Parameters8.6. Exchangeability and Parameters8.7. Mystery of Parameters9.  Statistical and Physical Models <9.1. The Idea9.2. The Best Model9.3. Second-Best Models9.4. Relevance and Importance9.5. Measurement9.6. Hypothesis Testing9.7. Die, P-Value, Die, Die, Die9.8. Implementing Statistical Models9.9. Model Goodness9.10. Decisions10.  Modeling Goals, Strategies, and Mistakes 10.1. Regression10.2. Risk10.3. Epidemiologist Fallacy10.4. Quantifying the Unquantifiable10.5. Time Series10.6. The Future

Détails

Code EAN :9783319819587
Auteur(trice): 
Editeur :Springer International Publishing-Springer Nature Switzerland-Springer International Publishing AG
Date de publication :  30-05-2018
Format :Livre de poche
Langue(s) : anglais
Hauteur :235 mm
Largeur :155 mm
Epaisseur :16 mm
Poids :429 gr
Stock :Impression à la demande (POD)
Nombre de pages :280
Mots clés :  Epistemology; Evidence; Philosophy; Probability; Statistics; cause; logic; modeling; models; philosophy of uncertainty