Hopsy - python 中凸多面体采样的方法市场。hopsy - a methods marketplace for convex polytope sampling in Python.-医云文献数字医云科研云海量医学决策数据服务

Abstract：

CONCLUSIONS: Effective collaboration between developers of Bayesian inference methods and users is key to advance our quantitative understanding of biosystems. We here present hopsy, 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 library HOPS, hopsy inherits 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 showcase hopsy by solving common and newly composed domain-specific sampling problems, highlighting important design choices. By likening hopsy to 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.
METHODS: Sources, documentation and a continuously updated list of sampling algorithms are available at https://jugit.fz-juelich.de/IBG-1/ModSim/hopsy, with Linux, Windows and MacOS binaries at https://pypi.org/project/hopsy/.

摘要：

结论：贝叶斯推理方法的开发者和用户之间的有效合作是推进我们对生物系统定量理解的关键。我们在这里介绍Hopsy，一个通用的开源平台，旨在方便地访问强大的马尔可夫链蒙特卡洛采样算法，该算法针对在凸多面体（CP）上定义的模型而定制。基于高性能C++采样库HOPS,hopsy继承了其优势，并通过Python编程语言的可访问性扩展了其功能。通用的插件机制可实现与特定领域模型的无缝集成，为方法开发人员提供测试框架，基准测试,并分发CP采样器以接近现实世界的推理任务。我们通过解决常见和新组成的特定领域采样问题来展示希望，突出重要的设计选择。把Hopsy比作市场，我们强调它在将用户和开发人员聚集在一起方面的作用，用户可以访问最先进的方法，和开发人员为挑战特定领域的推理问题贡献自己的创新解决方案。
方法：来源，文档和不断更新的采样算法列表可在https://jugit。FZ-Juelich.de/IBG-1/ModSim/hopsy,用Linux,Windows和MacOS二进制文件位于https://pypi.org/project/hopsy/。
背景：补充数据可在Bioinformatics在线获得。