“Behind every great point estimate stands a minimized loss function.” –

Me, just now

This is a continuation of *Probable Points and Credible Intervals*, a series of posts on Bayesian point and interval estimates. In Part 1 we looked at these estimates as *graphical summaries*, useful when it’s difficult to plot the whole posterior in good way. Here I’ll instead look at points and intervals from a decision theoretical perspective, in my opinion the conceptually cleanest way of characterizing what these constructs *are*.

If you don’t know that much about Bayesian decision theory, just chillax. When doing Bayesian data analysis you get it “pretty much for free” as esteemed statistician Andrew Gelman puts it. He then adds that it’s “not quite right because it can take effort to define a reasonable utility function.” Well, perhaps not free, but it is still *relatively* straight forward! I will use a toy problem to illustrate how Bayesian decision theory can be used to produce point estimates and intervals. The problem is this: Our favorite droid has gone missing and we desperately want to find him!