Uncertainty, Softcover reprint of the original 1st ed. 2016 The Soul of Modeling, Probability & Statistics
Auteur : Briggs William
1. Truth, Argument, Realism
1.1. Truth
1.2. Realism1.3. Epistemology
1.4. Necessary & Conditional Truth1.5. Science & Scientism
1.6. Faith1.7. Belief & Knowlege
2. Logic2.1. Language
2.2. Logic Is Not Empirical2.3. Syllogistic Logic
2.4. Syllogisms2.5. Informality
2.6. Fallacy
3. Induction and Intellection
3.1. Metaphysics
3.2. Types of Induction
3.3. Grue
4. What Probability Is
4.1. Probability Is Conditional
4.2. Relevance
4.3. The Proportional Syllogism
4.4. Details
4.5. Assigning Probability
4.6. Weight of Probability
4.7. Probability Usually Is Not a Number
4.8. Probability Can Be a Number
5. What Probability Is Not
5.1. Probability Is Not Physical5.2. Probability & Essence
5.3. Probability Is Not Subjective5.4. Probability Is Not Only Relative Frequency
5.5. Probability Is Not Always a Number Redux6. Chance and Randomness
6.1. Randomness6.2. Not a Cause
6.3. Experimental Design & Randomization6.4. Nothing Is Distributed
6.5. Quantum Mechanics6.6. Simulations
6.7. Truly Random & Information Theory
7. Causality
7.1. What Is Cause Like?
7.2. Causal Models
7.3. Paths
7.4. Once a Cause, Always a Cause
7.5. Falsifiability
7.6. Explanation
7.7. Under-Determination
8. Probability Models
8.1. Model Form
8.2. Relevance & Importance
8.3. Independence versus Irrelevance
8.4. Bayes
8.5. The Problem and Origin of Parameters
8.6. Exchangeability and Parameters
8.7. Mystery of Parameters
9. Statistical and Physical Models <
9.1. The Idea
9.2. The Best Model9.3. Second-Best Models
9.4. Relevance and Importance9.5. Measurement
9.6. Hypothesis Testing9.7. Die, P-Value, Die, Die, Die
9.8. Implementing Statistical Models9.9. Model Goodness
9.10. Decisions10. Modeling Goals, Strategies, and Mistakes
10.1. Regression
10.2. Risk
10.3. Epidemiologist Fallacy
10.4. Quantifying the Unquantifiable
10.5. Time Series
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.
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
Date de parution : 07-2016
Ouvrage de 258 p.
15.5x23.5 cm
Date de parution : 05-2018
Ouvrage de 258 p.
15.5x23.5 cm
Thème d’Uncertainty :
Mots-clés :
probability; statistics; philosophy; cause; evidence; models; modeling; epistemology; philosophy of uncertainty; logic