The Method of Anchored Distributions (MAD): Principles and Implementation as a Community Resource

锚定分布方法(MAD)：作为社区资源的原理和实现本项目将开发一个基于社区的、开源的计算平台，用于水文学中的贝叶斯逆模拟和条件模拟。这是因为需要为应用程序提供易于使用的工具和一个基础广泛的长期开发平台，以应对现代计算工具的日益复杂、数据类型的多样性以及应用程序所需主题的广度和深度带来的挑战。该项目的灵感来自于其他社区取得的成功，这些社区有着悠久的社区发展努力传统。我们将构建一个内核，它将包括一个模块化的计算平台和一个用户界面，允许用户和开发人员将内核与他们的模型联系起来。内核将使用开源架构构建。主要的重点将是开发一个独立的版本(用于用户-S计算机的大规模应用程序)。该内核将与广泛的基于社区的资源(如CUAHSI？S水文信息系统)和图书馆(如R)互操作。有了这些概念和原则，各种类型的数据可以被同化，简化的假设可以由用户自行决定，强大的数值模型和分析工具，无论是当前的还是正在开发的，可以作为基于社区的长期发展的焦点。项目开发人员将与CUAHSI协调该项目。CUHASI将作为MAD计算平台的托管人。这意味着保护MAD内核的完整性，对其进行更新，并使其可供用户和开发人员使用。这一努力符合CUAHSI的整体战略。MAD将通过向用户提供先进的逆向建模工具和为开发人员提供一个有吸引力的开发平台来促进科学发现过程。它将通过鼓励水文学家和地球科学家之间的合作来提高水文科学的质量，并为统计学家和计算机科学家提供一个平台。MAD将为教育工作者和学生提供获得现代、有充分记录的计算工具的途径，并激发他们进行实验，因为它将被理解为一种通用的、可扩展的工具，其用处远远超出课堂。MAD将为日益增长的基于社区的、开放源码的分析工具文化做出贡献，使科学更容易获得。长期存在的、经过充分检验的、透明的、因此更受信任的分析工具，构成了就与环境法规有关的事项进行建设性公共讨论的基础。

The Method of Anchored Distributions (MAD): Principles andImplementation as a Community ResourceThis project will develop a community-based, open-source computational platform for Bayesian inverse modeling and conditional simulations in hydrology. It is driven by the need to provide easily-accessible tools for applications and a platform for broad-based, long-term development, built to meet the challenges brought upon by the ever increasing complexity of modern computational tools, the diversity of data types and the breadth and depth of the subject matters needed for applications. The project is inspired by the success experienced by other communities with a long tradition of community-based development efforts. We will construct a kernel that will include a modular computational platform and a user-interface that will allow users and developers to link the kernel with their models. The kernel will be built using open-source architecture. The primary focus will be on developing a standalone version (for large-scale applications at the user?s computers). The kernel will be interoperable with a wide array of community-based resources (such as CUAHSI?s hydrologic information systems) and libraries (such as R). With these concepts and principles, various types of data could be assimilated, simplifying assumptions could be adopted at the user?s discretion, and powerful numerical models and analytical tools, current or under development, could be used as a focal point for long-term community-based development. The project developers will coordinate the project with CUAHSI. CUHASI will act as the custodian of the MAD computational platform. This implies securing the integrity of the MAD kernel, updating it and making it available to users and developers. This effort is in line with CUAHSI's overall strategy. MAD will facilitate the science discovery process by making advanced inverse modeling tools available to users and an inviting development platform for developers. It will enhance the quality of hydrologic science by encouraging collaboration between hydrologists and earth scientists, and provide a platform for statisticians and computer scientists. MAD will provide educators and students with access to modern, well-documented computational tools, and with the motivation to experiment with them, because it will be understood as a general and extensible tool that is useful far beyond the classroom. MAD will make a contribution to the growing culture of community-based, open-source analytical tools that make science more accessible. Long-standing, well-tested, transparent and hence better-trusted analytical tools form the base for a constructive public discourse on matters relating to environmental regulations.