Probabilistic Programming: Universal Languages, Systems and Applications

Probabilistic graphical models provide a formal lingua franca for modeling and a common target for efficient inference algorithms. Their introduction gave rise to an extensive body of work in machine learning, statistics, robotics, vision, biology, neuroscience, AI and cognitive science. However, many of the most innovative and exciting probabilistic models published by the NIPS community far outstrip the representational capacity of graphical models and are instead communicated using a mix of natural language, pseudo code, and mathematical formulae and solved using special purpose, one-off inference methods. Very often, graphical models are used only to describe the coarse, high-level structure rather than the precise specification necessary for automated inference. Probabilistic programming languages aim to close this representational gap; literally, users specify a probabilistic model in its entirety (e.g., by writing code that generates a sample from the joint distribution) and inference follows automatically given the specification. Several existing systems already satisfy this specification to varying degrees of expressiveness, compositionality, universality, and efficiency. We believe that the probabilistic programming language approach, which has been emerging over the last 10 years from a range of diverse fields including machine learning, computational statistics, systems biology, probabilistic AI, mathematical logic, theoretical computer science and programming language theory, has the potential to fundamentally change the way we understand, design, build, test and deploy probabilistic systems. The NIPS workshop will be a unique opportunity for this diverse community to meet, share ideas, collaborate, and help plot the course of this exciting research area.