Uncertainty - the soul of modeling, probability & statistics

Description

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 &lt 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