We present SlicStan — a probabilistic programming language that compiles to Stan and uses static analysis techniques to allow for more abstract and flexible models. SlicStan is novel in two ways: (1) it allows variable declarations and statements to be automatically shredded into different components needed for efficient Hamiltonian Monte Carlo inference, and (2) it introduces more flexible user-defined functions that allow for new model parameters to be declared as local variables. This work

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... monstrates that efficient automatic inference can be the result of the machine learning and programming languages communities joint efforts.