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hopsy - a methods marketplace for convex polytope sampling in Python.

Richard D Paul1,2, Johann F Jadebeck1,3, Anton Stratmann1,3

  • 1Institute of Bio- and Geosciences, IBG-1: Biotechnology, Forschungszentrum Jülich, 52428 Jülich, Germany.

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Summary
This summary is machine-generated.

Hopsy is a new open-source Python platform providing access to advanced Markov chain Monte Carlo (MCMC) sampling algorithms for Bayesian inference in biosystems. It facilitates collaboration between method developers and users for quantitative biological understanding.

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Area of Science:

  • Computational Biology
  • Bayesian Inference
  • Statistical Modeling

Background:

  • Advancing quantitative understanding of biosystems requires effective collaboration between Bayesian inference method developers and users.
  • Markov chain Monte Carlo (MCMC) sampling is crucial for complex biosystem modeling.
  • Current tools may lack accessibility or specific functionalities for models on convex polytopes (CP).

Purpose of the Study:

  • Introduce hopsy, a versatile open-source Python platform for MCMC sampling.
  • Facilitate access to powerful sampling algorithms tailored for models defined on convex polytopes (CP).
  • Bridge the gap between MCMC method developers and biosystem model users.

Main Methods:

  • Hopsy is built upon the high-performance C++ sampling library HOPS.
  • It extends HOPS functionalities with Python's accessibility.
  • A plugin mechanism allows seamless integration of domain-specific models and CP samplers.

Main Results:

  • Hopsy provides convenient access to state-of-the-art MCMC sampling algorithms.
  • The platform supports testing, benchmarking, and distribution of CP samplers.
  • Demonstrated hopsy's utility by solving common and novel domain-specific sampling problems.

Conclusions:

  • Hopsy acts as a collaborative marketplace connecting MCMC method developers and users.
  • It enhances the application of Bayesian inference in quantitative biosystem research.
  • Promotes innovation and accessibility in computational bioscience modeling.