Probabilistic programming promises to simplify and democratize probabilistic machine learning, but successful probabilistic programming systems require flexible, generic and efficient inference engines. In this talk I will present the probabilistic programming language called Turing which is under constant development together with the University of Cambridge, the University of Edinburgh and the University of Oxford. Turing has a very simple syntax and makes full use of the numerical capabilities in the Julia programming language, including all implemented probability distributions and automatic differentiation. Moreover, Turing supports a wide range of popular Monte Carlo algorithms including several Hamiltonian Monte Carlo (HMC) algorithms and various particle MCMC (PMCMC) samplers. Most importantly, Turing inference is composable: it combines MCMC operations on subsets of variables, for example using a combination of an HMC engine and a particle Gibbs (PG) engine.
Time: Monday, 10th of December 2018, 6:30 p.m. sharp
Location: Oesterreichisches Forschungsinstitut fuer Artificial Intelligence (OFAI), Freyung 6, Stiege 6, 1010 Wien
