I said it in an awkward way. The important thing is, your CP gizmo will tell you when something has gone haywire while you're using it. Your Bayes doodad might

I said it in an awkward way. The important thing is, your CP gizmo will tell you when something has gone haywire while you're using it. Your Bayes doodad might not (other than noticing errors). In particular, some very important new point may have a bad prediction associated with it, with bad error bars: CP may be of big help here. There's an example of this in page 102-106 of the original book I think. Another good example is applying this idea to HMMs which you can read about in "Hidden Markov Models with Confidence" Giovanni Cherubin1 and Ilia Nouretdinov I'm not throwing poo at Bayesian models, which I think are sadly neglected these days, but with CP ideas you can get more useful results. While I think CP is useful for practitioners now, the most exciting applications are in stuff like active learning, and developing novel techniques associated with this basket of ideas. Really, my blog kind of missed the mark. I need to do more with examples.