A Bayesian Model to Calculate Whether My Wife is Pregnant or Not by Rasmus Bååth.
From the post:
On the 21st of February, 2015, my wife had not had her period for 33 days, and as we were trying to conceive, this was good news! An average period is around a month, and if you are a couple trying to go triple, then a missing period is a good sign something is going on. But at 33 days, this was not yet a missing period, just a late one, so how good news was it? Pretty good, really good, or just meh?
To get at this I developed a simple Bayesian model that, given the number of days since your last period and your history of period onsets, calculates the probability that you are going to be pregnant this period cycle. In this post I will describe what data I used, the priors I used, the model assumptions, and how to fit it in R using importance sampling. And finally I show you why the result of the model really didn’t matter in the end. Also I’ll give you a handy script if you want to calculate this for yourself. 🙂
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I first saw this post in a tweet by Neil Saunders who commented:
One of the clearest Bayesian methods articles I’ve read (I struggle with most of them)
I agree with Neil’s assessment and suspect you will as well.
Unlike most Bayesian methods articles, this one also has a happy ending.
You should start following Rasmus Bååth on Twitter.