pymc

Further, for bayesian inference, Pymc 3 is more powerful and concise than training a model in stan.

In PyMC variables represent random variables, but these variables can't be used with Python constructs like conditionals and loops.

R has no native bayesian library like Pymc 3 (Must use stan which is c++).

With libraries like PyMC, it's one extra step beyond writing an assumption->outcome formula.

Sorry, I do not know PyMC well enough to say if the above quantities can be calculated also with it.

It details how to use the PyMC library via iPython notebooks.

Python has more consistent stats, time series and programming syntax (for the packages it does have), and better bayesian inference package (pymc 3 > stan).

This is in contrast to something like PyMC where random variables are very much special objects that interacted with, and making nonparametric models is trickier.

PyMC is a library implementing Bayesian inference through monte carlo methods.

I'll also point our that Anand (apatil) was a core developer for PyMC.

Do you have full support for numpy, scipy, matplotlib, pymc, and pandas?

In the wild I think you'd be more likely to use something like PyMC or Stan, which work like Prolly but support arbitrarily complex models.

The first few paragraphs of the link say it's based on PyMC, which itself appears in your link under "Existing probabilistic programming systems".

I don't buy Ian's argument that it's part of some latter type of category on the site, PyMC is directly linked in a section titled "Existing probabilistic programming systems".

Python argument, both have their strengths, but is it really true that R doesn't have anything like Pymc?