I’ve just started googling, so I have no prior knowledge to that topic. I am looking for a parametric (ideally 4 parameters) distribution with which I can model high moments like skewness and kurtosis.
I’ve found some references in a topic area Approximate Bayesian Computational methods (ABC methodology) that talk about g-and-k distributions. The following tutorial is a quick intro:
Does somebody know how to use such a g-and-k distribution in PyMC3? Or alternatively: does somebody know of another way that works in PyMC3 with which I can model a parametric distribution with higher moments?
Hi Christian,
I’m not versed in that area, but I know @aloctavodia works on the SMC-ABC sampler in PyMC3, so maybe he’ll be able to give you some pointers?
This is a long shot though, since, again, I don’t know this area
PyMC3 has a skewed normal distribution. And you also have the student t distribution. But I guess what you want is a skewed t distribution, unfortunately that distribution is not included with PyMC3, but you still can create it and use it with the help of pm.DensityDist.
