So I think I've genuinely finished http://gwern.net/Mail%20delivery now. It should be an interesting read for LWers: it's a fully Bayesian decision-theoretic analysis of when it is optimal to check my mail for deliveries. I learned a tremendous amount working my way through it, from how to much better use JAGS to how to do Bayesian model comparison & averaging to loss functions and EVSI and EVPI for decision theory purposes to even dabbling in reinforcement learning with Thompson sampling/probability-matching.

I thought it was done earlier, but then I realized I had messed up my Thompson sampling implementation and also vectorspace alien pointed out that my algorithm for deciding what datapoint to sample for maximizing information gain was incorrect & how to fix it, and I have made a lot of other small improvements like more images.