Full disclosure: my adviser reads this blog and was also the prof for the course. I don't think that's biasing my appraisal of the course, but it probably is, at least at some sub-conscious level.
This was really two courses. One was an upper undergrad course in applied Bayesian methods. The other was some directed readings and research so I could get credit for the class as a true graduate course. Since the thesis research part of it is unique to my case, I'll only discuss the part of the course that everybody took.
I wasn't sure coming in how applied the focus was going to be. It turned out to be pretty applied. Given the class composition, that was probably the way to go. I got the impression that most of the students were not math majors interested in the underlying theory. Instead, they were from other disciplines where they might have to actually use this stuff.
That would normally disappoint me but, the underlying theory of Bayesian stats really isn't that deep. It's the frequentist stuff that's all contorted to make up for the fact that so many problems have intractable Bayesian solutions.
Or, used to.
Now that almost any level of complexity in the model can be simulated, there really isn't much downside to just plowing ahead and letting the software packages do the work. Spending a semester learning how to set up and run arbitrarily complex models was time well spent. It was interesting to see that the applied part of the field was moving so fast that in the space of the semester, a new GLM (General Linear Model) package was released for R that pretty much obviated the last few chapters of the text.
The reliance on programming did make tests somewhat problematic. We got around that by simply not having any. As regular readers of this blog already know, I'm fine with that. I produce much better responses when I have time to think a problem through. My only complaint on that front was that we weren't given more to do. Not that I had tons of time on my hand this semester as I was really hammering on the research stuff, but the regular class assignments seemed a bit light.
Overall, I'm pretty happy with it. I really didn't realize how much computer modeling had revolutionized things. Thirty years ago, the Bayesian crowd was pretty marginalized because they had to make so many damaging assumptions to get their posterior distributions to converge. Freed from those limitations, the power of the method is obvious and it's quite powerful, indeed.
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