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hopsy - 在Python中用于凸多型样本的方法市场

Richard D Paul^1,2, Johann F Jadebeck^1,3, Anton Stratmann^1,3

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

Bioinformatics (Oxford, England)

|July 1, 2024

概括

霍普西 (Hopsy) 是一个新的开源Python平台,可访问高级马尔科夫链蒙特卡洛 (MCMC) 采样算法,用于生物系统中的贝叶斯推理. 它促进了方法开发人员和用户之间的合作,以获得定量生物学理解.

科学领域:

计算生物学计算生物学
贝叶斯的推理是贝叶斯的推理.
统计建模统计建模

背景情况:

提高对生物系统的定量理解需要贝叶斯推理方法开发人员和用户之间的有效合作.
马尔科夫链蒙特卡洛 (MCMC) 采样对于复杂的生物系统建模至关重要.
目前的工具可能缺乏可访问性或对凸多层 (CP) 模型的特定功能.

研究的目的:

介绍Hopsy,一个多功能开源的Python平台用于MCMC采样.
促进对强大的采样算法的访问,这些算法是为凸多层 (CP) 上定义的模型量身定制的.
弥合MCMC方法开发者和生物系统模型用户之间的差距.

主要方法:

霍普西是建立在高性能C++采样库HOPS的基础上.
它通过Python的可访问性来扩展HOPS的功能.
一个插件机制允许无集成域特定模型和CP样本.

主要成果:

霍普西可以方便地访问最先进的MCMC采样算法.
该平台支持CP样本的测试,比较和分发.
通过解决常见和新的域特定采样问题来证明hopsy的实用性.

结论:

霍普西作为一个协作市场,连接MCMC方法开发人员和用户.
它增强了贝叶斯推理在定量生物系统研究中的应用.
促进计算生物科学建模的创新和可访问性.

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