Bayesian stats in very few words

(but not short enough for a tweet)

I’ve long admired the capacity of Stata developers to encapsulate complex statistical methods in a few plain English words.

Now that their new release (version 14) includes some MCMC methods, they explain the world of Bayesian analysis in the leaflet thus:

Bayesian analysis is a statistical analysis that answers research questions about unknown parameters using probability statements. For example, what is the probability that the average male height is between 70 and 80 inches or that the average female height is between 60 and 70 inches?

That is very impressive. Yes, there’s other stuff you can say, but not without complicating it and discouraging the curious. This captures both one of the objectives and the fundamental technical difference, and one of the different ways in which results are interpreted.

I just had to blog that straight away, but there are two related things to look out for if you are crazy about Bayesy:

  1. As you might know, I’ve written a Stata-to-Stan interface. It is called StataStan and that has, to my delight, made me one of the Stan developers. It’s a bit like writing a little rhyme and suddenly being invited on tour with the Wu-Tang Clan. I will be posting a long explanation of it and some examples here very soon.
  2. Rasmus Bååth and I have a plan afoot to promote succinct introductions like this. When we have time (ha!) we will launch that online and hopefully get contributions from a few wise people (proverbial RZAs and GZAs but perhaps no ODBs)

mcmc-262x160

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