We present SPUX - a modular framework for Bayesian inference enabling uncertainty quantification and propagation in linear and nonlinear, deterministic and stochastic models, and supporting Bayesian model selection. SPUX can be coupled to any serial or parallel application written in any programming language, (e.g. including Python, R, Julia, C/C++, Fortran, Java, or a binary executable), scales effortlessly from serial runs on a personal computer to parallel high performance computing clusters, and aims to provide a platform particularly suited to support and foster reproducibility in computational science. We illustrate SPUX capabilities for a simple yet representative random walk model, describe how to couple different types of user applications, and showcase several readily available examples from environmental sciences. In addition to available state-of-the-art numerical inference algorithms including EMCEE, PMCMC (PF) and SABC, the open source nature of the SPUX framework and the explicit description of the hierarchical parallel SPUX executors should also greatly simplify the implementation and usage of other inference and optimization techniques.

中文翻译：

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SPUX框架：用于贝叶斯不确定性量化和传播的可扩展程序包

我们提出了SPUX-贝叶斯推理的模块化框架，可在线性和非线性，确定性和随机模型中进行不确定性量化和传播，并支持贝叶斯模型的选择。SPUX可以耦合到以任何编程语言编写的任何串行或并行应用程序（例如，包括Python，R，Julia，C / C ++，Fortran，Java或二进制可执行文件），可以轻松地从个人计算机上的串行运行扩展到并行高性能计算集群，旨在提供一个特别适合支持和促进计算科学重现性的平台。我们将说明一个简单但具有代表性的随机游走模型的SPUX功能，描述如何耦合不同类型的用户应用程序，并展示一些环境科学中容易获得的示例。