Symbolic Statistics with SymPy

@article{Rocklin2012SymbolicSW,
  title={Symbolic Statistics with SymPy},
  author={Matthew Rocklin and Andy R. Terrel},
  journal={Computing in Science \& Engineering},
  year={2012},
  volume={14},
  pages={88-93}
}
Replacing symbols with random variables makes it possible to naturally add statistical operations to complex physical models. Three examples of symbolic statistical modeling are considered here, using new features from the popular SymPy project. 
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References

SHOWING 1-4 OF 4 REFERENCES

From Equations to Code: Automated Scientific Computing

  • A. Terrel
  • Computer Science
    Computing in Science & Engineering
  • 2011
Using domain-specific languages, scientific codes can let users work directly with equations and benefit from optimizations not available with general compilers.

Probabilistic Functional Programming in Haskell

TLDR
This paper will demonstrate such an approach by describing a probabilistic functional programming (PFP) library for Haskell and show that the proposed approach not only facilitates probabilism programming in functional languages, but in particular can lead to very concise programs and simulations.

Functional Pearls: Probabilistic functional programming in Haskell

TLDR
Functional programming's principle of referential transparency is violated when contemplating the use of functions to describe probabilistic events, such as rolling a die, but these two seemingly incompatible notions can be reconciled if Probabilistic values are encapsulated in a data type.

Open source computer algebra systems: SymPy

This survey will look at SymPy, a free and open source computer algebra system started in 2005 by the second author (O.Č.). It is written entirely in Python, available from http://sympy.org. SymPy is…