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[Symbolic PyMC](https://pymc-devs.github.io/symbolic-pymc) provides tools for the symbolic manipulation of [PyMC](https://github.com/pymc-devs) models and their underlying computational graphs in [Theano](https://github.com/Theano/Theano) and [TensorFlow](https://github.com/tensorflow/tensorflow). It enables graph manipulations in the relational DSL [miniKanren](http://minikanren.org/)—via the [`kanren`](https://github.com/logpy/logpy) package—by way of meta classes and S-expression forms of a graph.
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[Symbolic PyMC](https://pymc-devs.github.io/symbolic-pymc) provides tools for the symbolic manipulation of [PyMC](https://github.com/pymc-devs) models and their underlying computational graphs in [Theano](https://github.com/Theano/Theano) and [TensorFlow](https://github.com/tensorflow/tensorflow). It enables graph manipulations in the relational DSL [miniKanren](http://minikanren.org/)—via the [`miniKanren`](https://github.com/pythological/kanren) package—by way of meta classes and S-expression forms of a graph.
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This work stems from a series of articles starting [here](https://brandonwillard.github.io/a-role-for-symbolic-computation-in-the-general-estimation-of-statistical-models.html). Documentation and examples for Symbolic PyMC are available [here](https://pymc-devs.github.io/symbolic-pymc).
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*This package is currently in alpha, so expect large-scale changes at any time!*
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## Installation
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The package name is `symbolic_pymc` and it can be installed with `pip` directly from GitHub
or after cloning the repo (and then installing with `pip`).
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## Features
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### General
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* Support for [Theano](https://github.com/Theano/Theano) and [TensorFlow](https://github.com/tensorflow/tensorflow) graphs
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-[Unification and reification](https://github.com/mrocklin/unification) for all components of a graph
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-[Unification and reification](https://github.com/pythological/unification) for all components of a graph
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- A more robust Theano `Op` for representing random variables
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- Conversion of PyMC3 models into sample-able Theano graphs representing all random variable inter-dependencies
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- A Theano LaTeX pretty printer that displays shape information and distributions in mathematical notation
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- Integrate standard Python operations into relations (e.g. use a symbolic math library to compute an inverse-Laplace transform to determine if a distribution is a scale mixture—and find its mixing distribution)
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* Convert graphs to an S-expression-like tuple-based form and perform manipulations at the syntax level
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* Pre-built example relations for graph traversal, fixed-points, symbolic closed-form posteriors, and standard statistical model reformulations
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## Installation
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The package name is `symbolic_pymc` and it can be installed with `pip` directly from GitHub
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