| Some Bayesian Approaches to Mixed Models | 13 |
13.1 Introduction and Background
13.2 P-Values and Some Alternatives
13.3 Bayes Factors and Posterior Probabilities of Null Hypotheses
13.4 Example: Teaching Methods
13.5 Generating a Sample from the Posterior Distribution with the PRIOR Statement
13.6 Example: Beetle Fecundity
Most readers are likely aware of the strong resurgence of Bayesian methods over the past two decades due largely to the development and availability of efficient algorithms for generating samples from Bayesian posterior probability distributions, such as Markov Chain Monte Carlo (Gelfand et al. 1990, Tierney 1994, Robert 1994). Computations are now less of a critical difficulty ...
Get SAS for Mixed Models, Second Edition, 2nd Edition now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
