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arXiv 2002.027022020-02-07cs.LGcs.PLstat.ML

DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models

University of New South Wales（新南威尔士大学）；University of Edinburgh（爱丁堡大学）；Graz University of Technology（格拉茨工业大学）；University of Cambridge（剑桥大学）；Uber AI Labs（优步人工智能实验室）

Mohamed Tarek, Kai Xu, Martin Trapp, Hong Ge, Zoubin Ghahramani

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We present the preliminary high-level design and features of DynamicPPL.jl, a modular library providing a lightning-fast infrastructure for probabilistic programming. Besides a computational performance that is often close to or better than Stan, DynamicPPL provides an intuitive DSL that allows the rapid development of complex dynamic probabilistic programs. Being entirely written in Julia, a high-level dynamic programming language for numerical computing, DynamicPPL inherits a rich set of features available through the Julia ecosystem. Since DynamicPPL is a modular, stand-alone library, any probabilistic programming system written in Julia, such as Turing.jl, can use DynamicPPL to specify models and trace their model parameters. The main features of DynamicPPL are: 1) a meta-programming based DSL for specifying dynamic models using an intuitive tilde-based notation; 2) a tracing data-structure for tracking RVs in dynamic probabilistic models; 3) a rich contextual dispatch system allowing tailored behaviour during model execution; and 4) a user-friendly syntax for probabilistic queries. Finally, we show in a variety of experiments that DynamicPPL, in combination with Turing.jl, achieves computational performance that is often close to or better than Stan.

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