I'm trying to reproduce this example from Confidence intervals vs Bayesian intervals by ET Jaynes: Assuming dispersion is standard deviation, how does one model that in PyMC? If I had the 31 + 61 samples, I'd just set them to the observed values of a generative model such as the one explained by Kruschke on his BEST procedure.
One idea I had was to create a deterministic function that would calculate the "dispersion" (standard deviation) of each group and set the two values to be observed. However, I don't know how to use the information about the 31 and 61 samples in this case. Ignoring sample size seems wrong to me, so I'm stuck.
Is it possible to model something like that in PyMC?