Résumé
There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most introductory statistics texts only present frequentist methods. In Bayesian statistics the rules of probability are used to make inferences about the parameter. Prior information about the parameter and sample information from the data are combined using Bayes theorem. Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used. This book uniquely covers the topics usually found in a typical introductory statistics book but from a Bayesian perspective.
L'auteur - William M. Bolstad
William M. Bolstad, PhD, is a Senior Lecturer in the Department of Statistics at the University of Waikato, New Zealand. He holds degrees from the University of Missouri, Stanford University, and the University of Waikato, New Zealand.
Sommaire
- Introduction to Statistical Science
- Scientific Data Gathering
- Displaying and Summarizing Data
- Logic, Probability, and Uncertainty
- Discrete Random Variables
- Bayesian Inference for Discrete Random Variables
- Continuous Random Variables
- Bayesian Inference for Binomial Proportion
- Comparing Bayesian and Frequentist Inferences for Proportion
- Bayesian Inference for Normal Mean
- Comparing Bayesian and Frequentist Inferences for Mean
- Bayesian Inference for Difference between Means
- Bayesian Inference for Simple Linear Regression
- Robust Bayesian Methods
Caractéristiques techniques
| PAPIER | |
| Éditeur(s) | Wiley |
| Auteur(s) | William M. Bolstad |
| Parution | 10/06/2004 |
| Nb. de pages | 354 |
| Format | 16 x 24 |
| Couverture | Relié |
| Poids | 660g |
| Intérieur | Noir et Blanc |
| EAN13 | 9780471270201 |
| ISBN13 | 978-0-471-27020-1 |
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