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| -rw-r--r-- | gnu/packages/statistics.scm | 41 |
1 files changed, 41 insertions, 0 deletions
diff --git a/gnu/packages/statistics.scm b/gnu/packages/statistics.scm index b60fdbc841..0f9f25989c 100644 --- a/gnu/packages/statistics.scm +++ b/gnu/packages/statistics.scm @@ -1328,6 +1328,47 @@ independence and others) @end itemize") (license license:bsd-3))) +(define-public python-zeus-mcmc + (package + (name "python-zeus-mcmc") + (version "2.5.4") + (source + (origin + (method git-fetch) ; no tests in PyPI + (uri (git-reference + (url "https://github.com/minaskar/zeus") + (commit version))) + (file-name (git-file-name name version)) + (sha256 + (base32 "0sci442fx2bkkj8169hwnx6psl7m2r8y1cicn1xjxxgqaby5j8pi")))) + (build-system pyproject-build-system) + (native-inputs + (list python-pytest + python-setuptools + python-wheel)) + (propagated-inputs + (list python-matplotlib + python-numpy + python-scikit-learn + python-scipy + python-seaborn + python-setuptools + python-tqdm)) + (home-page "https://github.com/minaskar/zeus") + (synopsis "Deep learning energy measurement and optimization framework") + (description + "This package provides an implementation of the Ensemble Slice Sampling method. +Features: +@itemize +@item fast & Robust Bayesian Inference +@item efficient Markov Chain Monte Carlo (MCMC) +@item black-box inference, no hand-tuning +@item excellent performance in terms of autocorrelation time and convergence rate +@item scale to multiple CPUs without any extra effort +@item automated Convergence diagnostics +@end itemize") + (license license:asl2.0))) + (define-public r-rversions (package (name "r-rversions") |