_            _    _        _         _
      /\ \         /\ \ /\ \     /\_\      / /\
      \_\ \       /  \ \\ \ \   / / /     / /  \
      /\__ \     / /\ \ \\ \ \_/ / /     / / /\ \__
     / /_ \ \   / / /\ \ \\ \___/ /     / / /\ \___\
    / / /\ \ \ / / /  \ \_\\ \ \_/      \ \ \ \/___/
   / / /  \/_// / /   / / / \ \ \        \ \ \
  / / /      / / /   / / /   \ \ \   _    \ \ \
 / / /      / / /___/ / /     \ \ \ /_/\__/ / /
/_/ /      / / /____\/ /       \ \_\\ \/___/ /
\_\/       \/_________/         \/_/ \_____\/
python-zeus-mcmc 2.5.4
Propagated dependencies: python-matplotlib@3.8.2 python-numpy@1.26.4 python-scikit-learn@1.7.0 python-scipy@1.12.0 python-seaborn@0.13.2 python-setuptools@80.9.0 python-tqdm@4.67.1
Channel: guix
Location: gnu/packages/statistics.scm (gnu packages statistics)
Home page: https://github.com/minaskar/zeus
Licenses: ASL 2.0
Synopsis: Deep learning energy measurement and optimization framework
Description:

This package provides an implementation of the Ensemble Slice Sampling method. Features:

  • fast & Robust Bayesian Inference

  • efficient Markov Chain Monte Carlo (MCMC)

  • black-box inference, no hand-tuning

  • excellent performance in terms of autocorrelation time and convergence rate

  • scale to multiple CPUs without any extra effort

  • automated Convergence diagnostics

Total results: 1