Statistical decision theory and bayesian analysis by James O. Berger
Statistical decision theory and bayesian analysis James O. Berger ebook
ISBN: 0387960988, 9780387960982
Numerical Analysis for Statisticians. No subjective decisions need to be involved. In contrast, "subjectivist" statisticians deny the Justification of Bayesian probabilities. Chen, Approximate Kalman Filtering, World Scientific, 1993. Solodovnikov, Introduction to the Statistical Dynamics of Automatic Control Systems, Dover, 1960. Statistical Decision Theory and Bayesian Analysis. While reading Chapter 22 of your book, Bayesian Data Analysis (2nd ed.) – I came upon the section on the *Distinction between decision analysis and 'statistical decision theory'* (p. Peter de Blanc, I don't have an example, just a vague memory of reading about minimax-optimal decision rules in J. Berger's Statistical Decision Theory and Bayesian Analysis. In the objectivist stream, the statistical analysis depends on only the model assumed and the data analysed. Research in Bayesian analysis and statistical decision theory is rapidly expanding and diversifying, making it increasingly more difficult for any single researcher to stay up to date on all current research frontiers. The use of Bayesian probabilities as the basis of Bayesian inference has been supported by several arguments, such as the Cox axioms, the Dutch book argument, arguments based on decision theory and de Finetti's theorem.