Automatic Reparameterisation of Probabilistic Programs

7 Jun 2019Maria I. GorinovaDave MooreMatthew D. Hoffman

Probabilistic programming has emerged as a powerful paradigm in statistics, applied science, and machine learning: by decoupling modelling from inference, it promises to allow modellers to directly reason about the processes generating data. However, the performance of inference algorithms can be dramatically affected by the parameterisation used to express a model, requiring users to transform their programs in non-intuitive ways... (read more)

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