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DVC is a free and open-source, platform-agnostic version system for data, machine learning models, and experiments. [1] It is designed to make ML models shareable, experiments reproducible, [2] and to track versions of models, data, and pipelines. [3] [4] [5] DVC works on top of Git repositories [6] and cloud storage. [7]
OpenBUGS is the open source variant of WinBUGS (Bayesian inference Using Gibbs Sampling). It runs under Microsoft Windows and Linux , as well as from inside the R statistical package . Versions from v3.0.7 onwards have been designed to be at least as efficient and reliable as WinBUGS over a range of test applications.
PVCS Version Manager (originally named Polytron Version Control System) is a software package by Serena Software Inc., for version control of source code files. PVCS follows the "locking" approach to concurrency control; it has no merge operator built-in (but does, nonetheless, have a separate merge command).
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.
Infer.NET is a free and open source.NET software library for machine learning. [2] It supports running Bayesian inference in graphical models and can also be used for probabilistic programming . [ 3 ]
Los Angeles-based Inference was founded in 1979. [3] In the 1990s they built a case-based computer program for Compaq Computer Corporation that would enable dealing with a situation where "a computer printer turns out a blurry and smeared page" without having to call a help desk. [1]
The software has a built-in web interface, which reduces project tracking complexity and promotes situational awareness. A user may simply type "fossil ui" from within any check-out and Fossil automatically opens the user's web browser to display a page giving detailed history and status information on that project.
[1] [2] Dagum developed DBNs to unify and extend traditional linear state-space models such as Kalman filters, linear and normal forecasting models such as ARMA and simple dependency models such as hidden Markov models into a general probabilistic representation and inference mechanism for arbitrary nonlinear and non-normal time-dependent domains.