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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) – 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)
More recently, other languages to support Bayesian model specification and inference allow different or more efficient choices for the underlying Bayesian computation, and are accessible from the R data analysis and programming environment, e.g.: Stan, NIMBLE and NUTS. The influence of the BUGS language is evident in these later languages ...
Stan is a probabilistic programming language for statistical inference written in C++; ArviZ a Python library for exploratory analysis of Bayesian models; Bambi is a high-level Bayesian model-building interface based on PyMC
Exploratory analysis of Bayesian models is an adaptation or extension of the exploratory data analysis approach to the needs and peculiarities of Bayesian modeling. In the words of Persi Diaconis: [16] Exploratory data analysis seeks to reveal structure, or simple descriptions in data. We look at numbers or graphs and try to find patterns.
ArviZ also provides a common data structure for manipulating and storing data commonly arising in Bayesian analysis, like posterior samples or observed data. ArviZ is an open source project, developed by the community and is an affiliated project of NumFOCUS .
Free and open-source software portal; Pages in category "Free Bayesian statistics software" ... Stan (software)
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.