hopsy - a methods marketplace for convex polytope sampling in Python

Abstract

AbstractSummaryEffective collaboration between developers of Bayesian inference methods and users is key to advance our quantitative understanding of biosystems. We here presenthopsy, a versatile open source platform designed to provide convenient access to powerful Markov chain Monte Carlo sampling algorithms tailored to models defined on convex polytopes (CP). Based on the high-performance C++ sampling libraryHOPS,hopsyinherits its strengths and extends its functionalities with the accessibility of the Python programming language. A versatile plugin-mechanism enables seamless integration with domain-specific models, providing method developers with a framework for testing, benchmarking, and distributing CP samplers to approach real-world inference tasks. We showcasehopsyby solving common and newly composed domain-specific sampling problems, highlighting important design choices. By likeninghopsyto a marketplace, we emphasize its role in bringing together users and developers, where users get access to state-of-the-art methods, and developers contribute their own innovative solutions for challenging domain-specific inference problems.Availability and ImplementationSources, documentation and a continuously updated list of sampling algorithms are available athttps://jugit.fz-juelich.de/IBG-1/ModSim/hopsy, with Linux, Windows and MacOS binaries athttps://pypi.org/project/hopsy/.Contactk.noeh@fz-juelich.de