Lambda the Ultimate

The recent developments around non-deterministic and probabilistic programming (aka weighted non-determinism) are very exciting. There are a lot of different implementation techniques with different strengths and weaknesses, like importance sampling, Gibbs sampling, belief propagation, etc. On the other hand, techniques for SAT solving like unit propagation, nogood learning and symmetry reduction have not yet been applied to probabilistic programming (as far as I know). Also, frequentist inference techniques like maximum likelihood & Bayesian loss function minimization can probably be done in the continuous case with automatic differentiation. Perhaps the bag of implementation techniques will continue to expand, or perhaps they will be unified into a single technique that combines the different strengths. In any case this direction of research is very exciting because the way you specify a generative model is often even higher level than the English prose you find in machine learning papers, yet you can automatically and generically do inference with them instead of having to hire a statistician and a computer scientist to develop inference algorithms. If the inference algorithms become good enough then that could transform how business decision making and experimental science are done.

Time to become a Contributing Editor?

Are there any comprehensive texts people here would recommend on probabilistic programming as a general topic? Is this something that's sitting in the primary literature at the moment?