janwillem

Just to clear up further confusion: Frank was indeed at Oxford previously – he moved to UBC this Spring. The tutorial actually took place at NIPS 2015 in Montreal.

Just a quick correction – Frank Wood is not at Microsoft, but at UBC:

http://www.cs.ubc.ca/~fwood/index.html

Microsoft Research does have multiple excellent researchers working on probabilistic programming. Infer.NET in particular is a highly advanced piece of technology for models in which you would use message passing algorithms to perform inference:

http://infernet.azurewebsites.net

Anglican dev here. We always welcome feedback. Could you explain what you find strange about the workflows?

We taught a summer school on Anglican last August. The materials are available online:

https://bitbucket.org/probprog/ppaml-summer-school-2016 http://www.ccs.neu.edu/home/jwvdm/talks/

Anglican dev here.

Systems like Anglican perform Bayesian inference in user-defined models, which are expressed as programs in a general-purpose language – in our case a subset of Clojure. An inference back end then implements a number of generic sampling-based algorithms like Metropolis-Hastings, sequential Monte Carlo and black box variational inference.

From the description, PAMELA provides a probabilistic version of a process modeling language. This is a concept that I'm personally not familiar with, but the intended use case appears to be the modeling of control systems, where inference is performed over some form of latent states in the control process:

http://www.ai.mit.edu/projects/ddamba/publications/RMPL.pdf

I'd be interested to hear from one of the devs!