LaplacesDemonCpp at GitHub: Development of LaplacesDemonCpp.LaplacesDemon at GitHub: Development of LaplacesDemon.^ Pierre-Simon Laplace, " A Philosophical Essay on Probabilities" (full text).Īll links below are broken.Archived from the original on 26 February 2014. International Journal of Computer Networking, Wireless and Mobile Communications. "A Study of Statistical Inference Tools for Uncertainty Reasoning in Target Tracking". ^ Maurya, M Vishwakarma, UK Lohia, P (2013).Monthly Notices of the Royal Astronomical Society. "Assessing luminosity correlations via cluster analysis: Evidence for dual tracks in the radio/X-ray domain of black hole X-ray binaries". "Strategies for Fitting Nonlinear Ecological Models in R, AD Model Builder, and BUGS". ^ 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).Pierre-Simon Laplace alluded to this hypothetical being in the introduction to his Philosophical Essay on Probabilities. The software was named after the concept of Laplace's demon, which refers to a hypothetical being capable of predicting the universe. An extension package called LaplacesDemonCpp is in development to provide C++ functionality. 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. 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 user writes their own model specification function and selects a numerical approximation algorithm to update their Bayesian model. LaplacesDemon has been used in numerous fields. LaplacesDemon is an open-source statistical package that is intended to provide a complete environment for Bayesian inference.
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