This book provides a multi-level introduction to Bayesian reasoning (as opposed to "conventional statistics") and its applications to data analysis. The basic ideas of this "new" approach to the quantification of uncertainty are presented using examples from research and everyday life. Applications covered include: parametric inference; combination of results; treatment of uncertainty due to systematic errors and background; comparison of hypotheses; unfolding of experimental distributions; upper/lower bounds in frontier-type measurements. Approximate methods for routine use are derived and a
Two major approaches have developed within Bayesian statistics to address uncertainty in the prior d...
This tutorial on Bayesian inference targets psychological researchers who are trained in the null hy...
This chapter provides a general overview of Bayesian statistical methods. Topics include the notion ...
This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal me...
This is a 20 page chapter for the upcoming Handbook of Statistical Systems Biology (D. Balding, M. S...
This new edition offers a comprehensive introduction to the analysis of data using Bayes rule. It ge...
This new edition offers a comprehensive introduction to the analysis of data using Bayes rule. It ge...
16 pages, 2 figures, 2 tables, chapter of the contributed volume "Bayesian Methods and Expert Elicit...
This book describes how Bayesian methods work. Its primary aim is to demystify them, and to show rea...
This article explains the foundational concepts of Bayesian data analysis using virtually no mathema...
This book presents Bayes' theorem, the estimation of unknown parameters, the determination of confid...
Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowled...
Bayesian Reasoning is both a fundamental idea of probability and a key model in applied sciences for...
This chapter focuses on Bayesian methods and illustrates both the intrinsic unity of Bayesian thinki...
Foundations of Bayesianism is an authoritative collection of papers addressing the key challenges th...
Two major approaches have developed within Bayesian statistics to address uncertainty in the prior d...
This tutorial on Bayesian inference targets psychological researchers who are trained in the null hy...
This chapter provides a general overview of Bayesian statistical methods. Topics include the notion ...
This chapter provides a overview of Bayesian inference, mostly emphasising that it is a universal me...
This is a 20 page chapter for the upcoming Handbook of Statistical Systems Biology (D. Balding, M. S...
This new edition offers a comprehensive introduction to the analysis of data using Bayes rule. It ge...
This new edition offers a comprehensive introduction to the analysis of data using Bayes rule. It ge...
16 pages, 2 figures, 2 tables, chapter of the contributed volume "Bayesian Methods and Expert Elicit...
This book describes how Bayesian methods work. Its primary aim is to demystify them, and to show rea...
This article explains the foundational concepts of Bayesian data analysis using virtually no mathema...
This book presents Bayes' theorem, the estimation of unknown parameters, the determination of confid...
Bayesian statistics is an approach to data analysis based on Bayes’ theorem, where available knowled...
Bayesian Reasoning is both a fundamental idea of probability and a key model in applied sciences for...
This chapter focuses on Bayesian methods and illustrates both the intrinsic unity of Bayesian thinki...
Foundations of Bayesianism is an authoritative collection of papers addressing the key challenges th...
Two major approaches have developed within Bayesian statistics to address uncertainty in the prior d...
This tutorial on Bayesian inference targets psychological researchers who are trained in the null hy...
This chapter provides a general overview of Bayesian statistical methods. Topics include the notion ...