Modular construction of Bayesian inference algorithms
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چکیده
We propose a set of abstractions to modularize implementation of Bayesian inference algorithms. We provide a proof-of-concept implementation as a Haskell library and demonstrate on several examples how it simplifies implementation of Monte Carlo algorithms. Our technique is based on a method for modular construction of interpreters using monad transformers and is applicable generically to probabilistic programming.
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تاریخ انتشار 2016