I like the links--thanks. About DIC and WAIC, etc. ... that's a huge topic for me. There's a relatively well-cited result that shows that you can arbitrarily

I like the links--thanks. About DIC and WAIC, etc. ... that's a huge topic for me. There's a relatively well-cited result that shows that you can arbitrarily choose any penalty in an information criterion (i.e., penalty in the form -logL + penalty), as long as it meets certain weak conditions, and it will eventually work with big enough samples. So the issues of most importance are usually how selection accuracy increases with sample size, not its asymptotic performance. The leads to a certain hesitancy for me in terms of why I should pay attention to any particular IC. Having said that, DIC and WAIC are pretty elegant in my mind, and I've been glad to see research on them. Predictive scenarios aren't really my focus, although I've become much more interested in them the last couple of years for various reasons. Most of my work is in what might be thought of as unsupervised models.