Here’s my version of the birthday frequency graph. I used Gaussian process with two slowly varying components and periodic component with decay, so that periodic form can change in time. I used Student’s t-distribution as observation model to allow exceptional dates to be outliers. I guess that periodic component due to week effect is still in the data because there is data only from twenty years. Naturally it would be better to model the whole timeseries, but it was easier to just use the cvs by Mulligan.
ALl I can say is . . . wow. Bayes wins again. Maybe Aki can supply the R or Matlab code?
P.S. And let’s not forget how great the simple and clear time series plots are, compared to various fancy visualizations that people might try.
P.P.S. More here.