The shortest answer to, "in what phase does anything Bayesian enter EMB?" is the inclusion of priors in the EM estimator. Below are increasingly more detailed discussions of this, as I was not certain the level of detail you were seeking (and do let me know if I still haven't reached that level of detail or clarity). In EMB, first, the original incomplete dataset is bootstrapped to create multiple (m) versions of the incomplete data. Second, on each bootstrapped dataset we then run the EM algorithm. However, (third) the implementation of the EM algorithm chosen by the user might include prior distributions for some of the parameters estimated by EM. At the convergence of each EM run a different set of estimates of all the parameters is obtained. Fourth, each version of this set of parameter estimates is used to impute one draw of all the missing values to create m imputed datasets in total. A schematic representation of these steps, that might be useful, is found in the user manual for Amelia that you have seen, in Figure 1 of: http://r.iq.harvard.edu/docs/amelia/amelia.pdf The paragraph that you mention in Honaker and King (2010) does not describe any priors on any parameters. There are many different reasons we might want to set priors, most common of which is to impose some shrinkage across the very large number of parameters needed in imputation models. Amelia allows the user to include setting a ridge (empirical) prior that shrinks the estimated covariance matrix of the dataset to a diagonal matrix (For discussion, see Ch 5.2 Schafer 1997, Ch 3.4.1 Hastie, Tibshirani and Friedman 2009, and 4.6.1 in the Amelia II manual). It is possible to conceptualize "ridge regression" simply as a form of Penalized maximum likelihood, thus we wouldn't strictly need to draw on any Bayesian foundation to implement this shrinkage. However, we also allow prior distributions for other and varied purposes in the Amelia package. For example Amelia allows priors on any of the parameters in the estimated covariance matrix of the data. Also, and much more usefully, the 2010 article later goes on to describe how to implement prior distributions on individual cell values in the incomplete data (p569-72, and Appendix 1). Our current research projects examine implementation and applications of these priors (for example as a treatment for measurement error, see Blackwell, Honaker King 2011 http://gking.harvard.edu/files/measure.pdf) Thus there are components of the Bootstrap, Likelihoodist and Bayesian systems of inference in the Amelia package. That can be disconcerting, although the original work of Rubin doesn't require a fully Bayesian estimator to generate imputations, just to meet the set of conditions he refers to as "proper." If a unified grounding is more satisfactory, from a Machine Learning perspective, the entire analysis--the first stage EMB algorithm, coupled with the recombination of second stage analysis estimates using Rubin's rules for MI--is simply a Bagging of the EM estimator (Ch 8.7 Hastie, Tibshirani and Friedman 2009). Let me know if anything simply creates more questions, James Honaker On Wed, Jun 29, 2011 11:32 AM David Kaplan <dkaplan(a)education.wisc.edu> wrote: - Amelia mailing list served by Harvard-MIT Data Center [Un]Subscribe/View Archive: http://lists.gking.harvard.edu/?info=amelia More info about Amelia: http://gking.harvard.edu/amelia