Anglican dev here. Systems like Anglican perform Bayesian inference in user-defined models, which are expressed as programs in a general-purpose language – in

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 http://www.ai.mit.edu/projects/ddamba/publications/RMPL.pdf I'd be interested to hear from one of the devs!