Buch, Englisch, 258 Seiten, Book, Format (B × H): 160 mm x 241 mm, Gewicht: 5443 g
The Soul of Modeling, Probability & Statistics
Buch, Englisch, 258 Seiten, Book, Format (B × H): 160 mm x 241 mm, Gewicht: 5443 g
ISBN: 978-3-319-39755-9
Verlag: Springer-Verlag GmbH
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.
Zielgruppe
Graduate
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik Mathematik Mathematik Allgemein Philosophie der Mathematik
- Geisteswissenschaften Philosophie Philosophie der Mathematik, Philosophie der Physik
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Mathematische Modellierung
- Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
- Mathematik | Informatik Mathematik Mathematik Allgemein Mathematische Logik
Weitere Infos & Material
1. Truth, Argument, Realism
1.1. Truth
1.2. Realism
1.3. Epistemology
1.4. Necessary & Conditional Truth
1.5. Science & Scientism
1.6. Faith
1.7. Belief & Knowlege
2. Logic
2.1. Language
2.2. Logic Is Not Empirical
2.3. Syllogistic Logic
2.4. Syllogisms
2.5. 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 Physical
5.2. Probability & Essence
5.3. Probability Is Not Subjective
5.4. Probability Is Not Only Relative Frequency
5.5. Probability Is Not Always a Number Redux
6. Chance and Randomness
6.1. Randomness
6.2. Not a Cause
6.3. Experimental Design & Randomization
6.4. Nothing Is Distributed
6.5. Quantum Mechanics
6.6. 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 Model
9.3. Second-Best Models
9.4. Relevance and Importance
9.5. Measurement
9.6. Hypothesis Testing
9.7. Die, P-Value, Die, Die, Die
9.8. Implementing Statistical Models
9.9. Model Goodness
9.10. Decisions
10. Modeling Goals, Strategies, and Mistakes
10.1. Regression
10.2. Risk
10.3. Epidemiologist Fallacy
10.4. Quantifying the Unquantifiable
10.5. Time Series