Not sure if it was generous grading or a big curve (probably both), but I wound up with an A on the Data Mining final. Grades for all the other work in the class were also published. As I expected, they were also A's, so the 4.0 stays in tact for now. Seems like if all that stuff could have been graded in the past two days, it could have been graded last week as well when it would have served a purpose. Ah, well, that's all I'll say on that.
I've also turned in my final project for Bayesian Stats even though it's not due until tomorrow. I'm going to spend the rest of this week catching up at work and then really go heads down on getting a decent draft of my CISS paper done. There's actually a bit more research I want to conduct on that front. Hopefully it will only take a few days. Basically, what I've found from messing around with it is that it works great until the percentage of rows hit within each block gets really low. Then the prior of Uniform(0,max possible) on the block total starts to mess up the estimate of the variance. So, I need to change that to a hierarchical model where the block sum is distrubuted U(0,X) and X is distributed via something else.
Not sure what that something else should be. Obviously, I don't want to pull it away from max possible too quickly or it will mess up the convergence of the bigger queries which are currently behaving quite nicely. And, of course, since we can't be running MCMC chains during query processing to get posteriors, it needs to be something with a tractable conjugate distribution.
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