Automating inference, learning, and design using probabilistic programming

More by this author Imagine a world where computational simulations can be inverted as easily as running them forwards, where data can be used to refine models automatically, and where the only expertise one needs to carry out powerful statistical analysis is a basic proficiency in scientific coding. Creating such a world is the ambitious long-term aim of probabilistic programming. The bottleneck for improving the probabilistic models, or simulators, used throughout the quantitative sciences... Inference in probabilistic logic programs using weighted CNFs. In Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence (UAI), AUAI Press, Corvallis, Oregon, USA, 211–220. Getoor, L. and Taskar, B. 2007. Learning the parameters of probabilistic logic programs from interpretations. In Proceedings of the 2008 European Conference on Machine Learning and Knowledge Discovery in Databases (ECML/PKDD), Part 1. Springer-Verlag, Berlin, Germany, 581–596. Ishihata, M., Kameya, Y., Sato, T. and Minato, S. 2008. 1.1 Automated Variational Inference for Probabilistic Programming. Probabilistic programming languages simplify the development of probabilistic models by allowing. programmers to specify a stochastic process using syntax that resembles modern programming lan 2 Automated Variational Inference. An unconditional probabilistic program f is defined as a parameterless function with an arbitrary. mix of deterministic and stochastic elements.