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gnu: Add python-nautilus-sampler.
* gnu/packages/statistics.scm (python-nautilus-sampler): New variable. Change-Id: Ic2881d9d26b02d7dd8ea03523a6a20195dd94727
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@@ -916,6 +916,51 @@ point (up to 50% contamination) and have a number of nice applications in
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machine learning, computer vision, and high-dimensional statistics.")
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(license license:asl2.0)))
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(define-public python-nautilus-sampler
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(package
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(name "python-nautilus-sampler")
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(version "1.0.5")
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(source
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(origin
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(method url-fetch)
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(uri (pypi-uri "nautilus_sampler" version))
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(sha256
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(base32 "1b73rxg7b5fzpw4ss4py98xdxddkl1dh2dszp2pxv3y179iyniqj"))))
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(build-system pyproject-build-system)
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(arguments
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(list
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#:test-flags
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#~(list "--durations=0"
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;; One Dask test hangs.
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"-k" "not test_pool[dask]")
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#:phases
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#~(modify-phases %standard-phases
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(add-before 'check 'pre-check
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(lambda _ (setenv "OMP_NUM_THREADS" "1"))))))
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(native-inputs
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(list python-dask
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python-distributed
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python-flit-core
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python-h5py
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python-pytest
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python-pytest-xdist))
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(propagated-inputs
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(list python-numpy
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python-scikit-learn
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python-scipy
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python-threadpoolctl))
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(home-page "https://github.com/johannesulf/nautilus")
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(synopsis "Neural Network-Boosted Importance Sampling for Bayesian Statistics")
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(description
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"Nautilus is an pure-Python package for Bayesian posterior and evidence
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estimation. It utilizes importance sampling and efficient space exploration
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using neural networks. Compared to traditional @acronym{MCMC, Markov chain
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Monte Carlo} and Nested Sampling codes, it often needs fewer likelihood calls
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and produces much larger posterior samples. Additionally, nautilus is highly
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accurate and produces Bayesian evidence estimates with percent precision. It
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is widely used in many areas of astrophysical research.")
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(license license:expat)))
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(define-public python-nestle
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(package
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(name "python-nestle")
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