Buch, Englisch, 500 Seiten, Format (B × H): 208 mm x 260 mm, Gewicht: 1520 g
Buch, Englisch, 500 Seiten, Format (B × H): 208 mm x 260 mm, Gewicht: 1520 g
ISBN: 978-1-108-83556-5
Verlag: Cambridge University Press
This textbook bypasses the need for advanced mathematics by providing in-text computer code, allowing students to explore Bayesian data analysis without the calculus background normally considered a prerequisite for this material. Now, students can use the best methods without needing advanced mathematical techniques. This approach goes beyond “frequentist” concepts of p-values and null hypothesis testing, using the full power of modern probability theory to solve real-world problems. The book offers a fully self-contained course, which demonstrates analysis techniques throughout with worked examples crafted specifically for students in the behavioral and neural sciences. The book presents two general algorithms that help students solve the measurement and model selection (also called “hypothesis testing”) problems most frequently encountered in real-world applications.
Autoren/Hrsg.
Fachgebiete
- Sozialwissenschaften Soziologie | Soziale Arbeit Soziologie Allgemein Empirische Sozialforschung, Statistik
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung
- Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Forschungsmethodik, Wissenschaftliche Ausstattung
Weitere Infos & Material
1. Logic and data analysis; 2. Mechanics of probability calculations; 3. Probability and information: from priors to posteriors; 4. Prediction and decision; 5. Models and measurements; 6. Model selection: Appendix A. Coding basics; Appendix B. Mathematics review: logarithmic and exponential function; Appendix C. The Bayesian toolbox: marginalization and coordinate transformations.