LaplacesDemon

LaplacesDemon
Developer(s) Statisticat, LLC.
Initial release 28 December 2010 (2010-12-28)
Stable release 15.03.19 / 19 March 2015 (2015-03-19)
Development status Active
Written in R programming language, C++
Operating system Unix-like, Microsoft Windows, Mac OS X
Available in English
Type Statistical package
License MIT License
Website bayesian-inference.com/software

LaplacesDemon is an open-source statistical package that is intended to provide a complete environment for Bayesian inference. LaplacesDemon has been used in numerous fields.[1][2][3] The user writes their own model specification function and selects a numerical approximation algorithm to update their Bayesian model. Some numerical approximation families of algorithms include Laplace's method (Laplace approximation), numerical integration (iterative quadrature), Markov chain Monte Carlo (MCMC), and Variational Bayes.

The base package, LaplacesDemon, is written entirely in the R programming language, and is largely self-contained, though it does require the parallel package for high performance computing via parallelism. Big data is also supported.[4] An extension package called LaplacesDemonCpp is in development to provide C++ functionality.[5]

The software was named after the concept of Laplace's demon, which refers to a hypothetical being capable of predicting the universe. Pierre-Simon Laplace alluded to this hypothetical being in the introduction to his Philosophical Essay on Probabilities.[6]

See also

References

  1. ↑ Bolker, BM; Gardner B, Maunder M, Berg CW, Brooks M, Comita L, Crone E, Cubaynes S, Davies T, de Valpine P, Ford J, Gimenez O, Kery M, Kim EJ, Lennert-Cody C, Magnusson A, Martell S, Nash J, Nielsen A, Regetz J, Skaug H, Zipkin E (2013). "Strategies for Fitting Nonlinear Ecological Models in R, AD Model Builder, and BUGS". Methods in Ecology and Evolution 4: 501–512. doi:10.1111/2041-210X.12044. Cite uses deprecated parameter |coauthors= (help);
  2. ↑ Gallo, E; Miller B; Fender R (2012). "Assessing luminosity correlations via cluster analysis: Evidence for dual tracks in the radio/X-ray domain of black hole X-ray binaries". Monthly Notices of the Royal Astronomical Society 423 (1): 590–599. doi:10.1111/j.1365-2966.2012.20899.x.
  3. ↑ Maurya, M; Vishwakarma, UK; Lohia, P (2013). "A Study of Statistical Inference Tools for Uncertainty Reasoning in Target Tracking". International Journal of Computer Networking, Wireless and Mobile Communications 3 (3): 1–10.
  4. ↑ "Big Data and Bayesian Inference". http://www.bayesian-inference.com. Statisticat, LLC. Retrieved 22 February 2014. External link in |work= (help)
  5. ↑ "C++ is Sugar for LaplacesDemon". http://www.bayesian-inference.com. Statisticat, LLC. Retrieved 8 May 2014. External link in |work= (help)
  6. ↑ Pierre-Simon Laplace, "A Philosophical Essay on Probabilities" (full text).

External links

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