A Bayesian "posterior distribution" or "predictive distribution" summarizes everything you need to know about an unknown parameter, or future observations....
Lire la suite
41,40 €
Expédié sous 2 à 4 semaines
Livré chez vous entre le 21 mai et le 4 juin
En magasin
Résumé
A Bayesian "posterior distribution" or "predictive distribution" summarizes everything you need to know about an unknown parameter, or future observations. This unique book shows how to use Bayesian statistical techniques in a sound and practically relevant manner. It will guide the reader on inferring scientific, medical, and social conclusions from numerical data. The authors explain the subtle assumptions needed for Bayesian methodology and show how to use them to obtain good-quality conclusions. The methods also perform remarkably well in terms of computer-simulated frequency properties. The lively introductory chapter on Fisherian methods (the frequency approach), together with a strong overall emphasis on likelihood, makes the text suitable for mainstream statistics courses whose instructors wish to follow mixed or comparative philosophies. A chapter on advances in utility theory, and several sections on time series and forecasting, makes the text also suitable for quantitative economics students. The other chapters contain material on the linear model, categorical data analysis, survival analysis, random-effects models, and nonlinear smoothing. The book contains numerous worked examples, self-study exercises, and practical applications. It provides essential reading for final-year undergraduates, Masters-degree and graduate students, statisticians, and other interdisciplinary researchers wishing to develop good-quality conclusions from their data and to pursue the notion of scientific truth.
Sommaire
Introductory Statistical Concepts
The Discrete Version of Bayes' Theorem
Models with a Single Unknown Parameter
The Expected Utility Hypothesis
Models with Several Unknown Parameters
Prior Structures, Posterior Smoothing, and Bayes-Stein Estimation