Sure, Pyro is very nice for some deep latent probabilistic models. E.g. deep HMMs. So great to model complex time series and space-state models. Edward and Tens

Sure, Pyro is very nice for some deep latent probabilistic models. E.g. deep HMMs. So great to model complex time series and space-state models. Edward and TensorFlow Probability are theoretically also good for this, but docs are a bit lacking. Turing is very user friendly, but in my experience really hard to scale to large hierarchical (multilevel) continuous variable models, which is what Stan was designed for. It can do lots of other things, like e.g. Bayesian NNs (Turing + Flux). And it's good for learning. I've prototyped lots of models on Turing, but I have had to eventually migrate them to Stan as I struggled a lot with efficiency once I tried to scale to a massive number of variables (think e.g. a proteomics experiment).