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Augur: a Modeling Language for Data-Parallel Probabilistic Inference
[article]
2014
arXiv
pre-print
It is time-consuming and error-prone to implement inference procedures for each new probabilistic model. Probabilistic programming addresses this problem by allowing a user to specify the model and having a compiler automatically generate an inference procedure for it. For this approach to be practical, it is important to generate inference code that has reasonable performance. In this paper, we present a probabilistic programming language and compiler for Bayesian networks designed to make
arXiv:1312.3613v2
fatcat:vi6klyjuhrbelh5hr3yqau6vsy