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Stan is a probabilistic programming language for statistical inference written in C++. [2] The Stan language is used to specify a (Bayesian) statistical model with an imperative program calculating the log probability density function .
Stan (software) – open-source package for obtaining Bayesian inference using the No-U-Turn sampler, a variant of Hamiltonian Monte Carlo. It is somewhat like BUGS, but with a different language for expressing models and a different sampler for sampling from their posteriors; Statistical Lab – R-based and focusing on educational purposes
Stan (software) – Stan is an open-source package for obtaining Bayesian inference using the No-U-Turn sampler (NUTS), [27] a variant of Hamiltonian Monte Carlo. PyMC – A Python library implementing an embedded domain specific language to represent bayesian networks, and a variety of samplers (including NUTS)
Bayesian programming [2] is a formal and concrete implementation of this "robot". Bayesian programming may also be seen as an algebraic formalism to specify graphical models such as, for instance, Bayesian networks, dynamic Bayesian networks, Kalman filters or hidden Markov models.
Bayesian experimental design provides a general probability-theoretical framework from which other theories on experimental design can be derived. It is based on Bayesian inference to interpret the observations/data acquired during the experiment. This allows accounting for both any prior knowledge on the parameters to be determined as well as ...
Sequential Bayesian filtering is the extension of the Bayesian estimation for the case when the observed value changes in time. It is a method to estimate the real value of an observed variable that evolves in time. There are several variations: filtering when estimating the current value given past and current observations, smoothing
ISO/IEC/IEEE 29119 Software and systems engineering -- Software testing [1] is a series of five international standards for software testing.First developed in 2007 [2] and released in 2013, the standard "defines vocabulary, processes, documentation, techniques, and a process assessment model for testing that can be used within any software development lifecycle."
Bayesian inference using Gibbs sampling (BUGS) is a statistical software for performing Bayesian inference using Markov chain Monte Carlo (MCMC) methods. It was developed by David Spiegelhalter at the Medical Research Council Biostatistics Unit in Cambridge in 1989 and released as free software in 1991.