I’ve shown in another post how the likelihood works as the updating factor for turning priors into posteriors for parameter estimation. In this post I’ll explain how using Bayes factors for model comparison can be conceptualized as a simple extension of likelihood ratios.
Doug asked for a top 10 list, and a few people have already chimed in with great suggestions. I thought those not on the list might also have good ideas, so, with Doug’s permission, I’m reposting the question here.
This year JSM features an interesting invited session about fitting joint models in different software packages — if you’re interested drop by… Here are my slides in which I give a short intro in packages JM and JMbayes: