I am curious because I have seen some members from their dev team tacking part in some of the github issues. Is this meant to be supplementary or complimentary to that library?
The other question would be a comparison between PyMC3 and PyStan. But I know the former is becoming deprecated along with theano and I believe the latter is not so modern.
Pyro vs Edward: like Edward, Pyro is a deep probabilistic programming language that focuses on variational inference but supports general composable inference algorithms. Pyro aims to be more dynamic (by using PyTorch) and universal (allowing recursion). In particular it should be easy to integrate existing simulator libraries into Pyro as long as they have a Python interface. The Pyro, Edward, and Probabilistic Torch teams are collaborating to identify common concepts and to ensure models are maximally compatible across our systems.
Pyro vs PyMC, Stan: Pyro embraces deep neural nets and currently focuses on variational inference. Pyro doesn’t do MCMC yet (but we’re actively working on HMC). Whereas Stan models are written in the Stan language, Pyro models are just python programs with pyro.sample() statements. Stan caters to the stats community who value interpretability and model criticism. Pyro caters more to the ML community, and tries to add some statistical tools to existing PyTorch models.
Thank you for your response.
Can you identify what factors should effect which one you choose? I thought edward also is focused on variational methods. Should MCMC be left to pymc3 then?
I saw probabilistic torch as well but 2 probabilistic programming languages is already enough for me! I do hope to be fluent enough in all of them eventually but i am still learning!