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Extending Stan for Deep Probabilistic Programming
Deep probabilistic programming combines deep neural networks (for automa...
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A Probabilistic Extension of Action Language BC+
We present a probabilistic extension of action language BC+. Just like B...
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NetworkDynamics.jl – Composing and simulating complex networks in Julia
NetworkDynamics.jl is an easy-to-use and computationally efficient packa...
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Transforming Probabilistic Programs for Model Checking
Probabilistic programming is perfectly suited to reliable and transparen...
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A Dynamic Programming Algorithm for Inference in Recursive Probabilistic Programs
We describe a dynamic programming algorithm for computing the marginal d...
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A scriptable, generative modelling system for dynamic 3D meshes
We describe a flexible, script-based system for the procedural generatio...
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Cinnamons: A Computation Model Underlying Control Network Programming
We give the easily recognizable name "cinnamon" and "cinnamon programmin...
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DynamicPPL: Stan-like Speed for Dynamic Probabilistic Models
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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