An Exponential Family Basis for Probabilistic Programming

Many common distributional families take the form of exponential families.
The functional form of the conditional densities of these families gives them convenient compositional properties, yielding opportunities for powerful reasoning and optimization.

We present a Haskell-based approach for expressing probabilistic models in terms of free arrows, over a basis of exponential families. Arrows are more restrictive than the more common monadic approach, but this sacrifice in expressiveness is balanced with broader opportunities for inference, for example in terms of the dependency graph of random variables. Moreover, any monadic inference method is easily applied to arrow-based models.

The extended abstract is available at

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