Using WinBUGS to implement Bayesian inferences of estimation and
testing hypotheses, Bayesian Methods for Measures of Agreement
presents useful methods for the design and analysis of agreement
studies. It focuses on agreement among the various players in the
diagnostic process. The author employs a Bayesian approach to
provide statistical inferences based on various models of intra-
and interrater agreement. He presents many examples that illustrate
the Bayesian mode of reasoning and explains elements of a Bayesian
application, including prior information, experimental information,
the likelihood function, posterior distribution, and predictive
distribution. The appendices provide the necessary theoretical
foundation to understand Bayesian methods as well as introduce the
fundamentals of programming and executing the WinBUGS software.
Taking a Bayesian approach to inference, this hands-on book
explores numerous measures of agreement, including the Kappa
coefficient, the G coefficient, and intraclass correlation. With
examples throughout and end-of-chapter exercises, it discusses how
to successfully design and analyze an agreement study.
General
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