LaplacesDemon explained

LaplacesDemon
Logo Size:	200px
Developer:	Statisticat, LLC.
Latest Release Version:	15.03.19
Programming Language:	R, C++
Operating System:	Unix-like, Microsoft Windows, Mac OS X
Language:	English
Genre:	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 Bayesian methods.

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

• Bayesian inference
• PyMC
• WinBUGS

External links

All links below are broken. New references are required.

• LaplacesDemon: Official website
• LaplacesDemon at GitHub: Development of LaplacesDemon
• LaplacesDemonCpp at GitHub: Development of LaplacesDemonCpp
• plus.google.com/+Bayesian-inference: Software updates have been announced here

Notes and 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. Strategies for Fitting Nonlinear Ecological Models in R, AD Model Builder, and BUGS. Methods in Ecology and Evolution. 2013. 4. 6. 501–512. 10.1111/2041-210X.12044. free.
2. Gallo. E. Miller B . Fender R . 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. 2012. 423. 1. 590–599. 10.1111/j.1365-2966.2012.20899.x. 1203.4263. 2012MNRAS.423..590G.
3. Maurya. M . Vishwakarma, UK . Lohia, P. A Study of Statistical Inference Tools for Uncertainty Reasoning in Target Tracking. International Journal of Computer Networking, Wireless and Mobile Communications. 2013. 3. 3. 1–10.
4. Web site: Big Data and Bayesian Inference. bayesian-inference.com. Statisticat, LLC.. 22 February 2014. https://web.archive.org/web/20140226003504/http://www.bayesian-inference.com/softwarearticlesbigdata. 26 February 2014. dead.
5. Web site: C++ is Sugar for LaplacesDemon. bayesian-inference.com. Statisticat, LLC.. 8 May 2014. https://web.archive.org/web/20140512222343/http://www.bayesian-inference.com/softwarearticlescppsugar. 12 May 2014. dead.
6. Pierre-Simon Laplace, "A Philosophical Essay on Probabilities" (full text).