Open CamDavidsonPilon opened 11 years ago
I didn't find it too much complicated for myself, and I thinks that's a quite satisfying example, which shows power of Bayesian tools.
I agree with @alexgarel. It is a small mental step from a model one lambda parameter (which is very trivial) to two parameters (which is very difficult in freq. methods, but simple here). I'll wait for another opinion/suggestion before I close this.
Well, I agree that it's too complicated. Text messaging is not really governed by Poisson processes. Wouldn't it make more sense to have data driven by a simple, unchanging Poisson process, and then use Bayesian inference to estimate lambda?
How about looking at photon arrival counts per minute for the Chandra space telescope (as I have suggested elsewhere)? These are well known to follow simple Poisson statistics. The Crab Nebula might be a good candidate, since Chandra uses it for calibration:
Deep field observations are even better, since they are much fainter.
Lovely picture. Estimating a single lambda is a trivial exercise. Multiple lambda with an unknown switchpoint is a difficult frequentist procedure, but simple in probabilistic programming, which is why I used it as a first example.
Perhaps I can put an smaller example, like the nebula example, before the text message example.
Sounds good; that way the reader gets warmed up with something easy/well behaved before encountering a far more complicated model.
It wasn't clear that you wanted a difficult-for-frequentist-statistics example. Here are a few others that are easy probabilistic programming activities, but hard for frequentist analysis:
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