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Symbolic Statistics with SymPy
May/June 2012 (vol. 14 no. 3)
pp. 88-93
ASCII Text
x 
 
Matthew Rocklin, Andy R. Terrel, "Symbolic Statistics with SymPy," Computing in Science and Engineering, vol. 14, no. 3, pp. 88-93, May/June, 2012.
 
 
BibTex
x
 
@article{ 10.1109/MCSE.2012.56,
author = {Matthew Rocklin and Andy R. Terrel},
title = {Symbolic Statistics with SymPy},
journal ={Computing in Science and Engineering},
volume = {14},
number = {3},
issn = {1521-9615},
year = {2012},
pages = {88-93},
doi = {http://doi.ieeecomputersociety.org/10.1109/MCSE.2012.56},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - MGZN
JO - Computing in Science and Engineering
TI - Symbolic Statistics with SymPy
IS - 3
SN - 1521-9615
SP88
EP93
EPD - 88-93
A1 - Matthew Rocklin,
A1 - Andy R. Terrel,
PY - 2012
KW - Computational science
KW - scientific computing
KW - SymPy
KW - Python
KW - symbolic statistical modeling
VL - 14
JA - Computing in Science and Engineering
ER -
 

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.

1. D. Joyner et al., "Open Source Computer Algebra Systems: SymPy," ACM Comm. Computer Algebra, vol. 45, nos. 3/4, 2011, pp. 225–234.
2. M. Erwig and S. Kollmansberger, "Probabilistic Functional Programming in Haskell," J. Functional Programming, vol. 16, no. 1, 2006, pp. 21–34.
3. Andy R. Terrel, "From Equations to Code: Automated Scientific Computing," Computing in Science & Eng., vol. 13, no. 2, 2011, pp. 78–82.

Index Terms:
Computational science, scientific computing, SymPy, Python, symbolic statistical modeling
Citation:
Matthew Rocklin, Andy R. Terrel, "Symbolic Statistics with SymPy," Computing in Science and Engineering, vol. 14, no. 3, pp. 88-93, May-June 2012, doi:10.1109/MCSE.2012.56
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