Collaborative Research: Computational Environment for Bayesian Inference in the Social Sciences

马尔可夫链蒙特卡罗（MCMC）方法的出现是近十五年来统计计算领域最重要的发展。虽然这些算法允许统计学家拟合几乎任何可以想象的模型，但统计学家一直是唯一能够充分利用这些估计方法的人（有明显的例外）。这个项目提供了一个计算环境，将MCMC方法交到社会科学家手中，这样他们也可以利用这些算法的力量来适应他们选择的创新统计模型。该项目为贝叶斯推理提供免费、开源、易于使用的软件，以满足社会科学家的需求。它还提供了一个文档化的开发环境，其他人可以使用它轻松地实现非标准的统计模型，并为其他研究人员提供了一种机制，使他们能够使用一致的用户界面和文档来分发自己的软件。该项目以提供和开发统计软件的科学方法为基础。软件开发是累积的，建立在他人工作的基础上；它是免费的、开源的和跨平台的，因此允许广泛的传播和极快的开发-发布周期。此外，由于所有用户都可以访问底层源代码，因此修复错误和扩展软件非常简单。该项目将强大的统计方法交到以经验为导向的社会和行为科学家手中。因此，它将提高在这些研究领域所做的实证工作的质量。更具体地说，该项目开发和提供用于统计学习的计算机软件。该软件利用最先进的算法，同时保持易于使用。该项目还包含一个教学组件，提供了一套用于本科和研究生教学的示范程序。此外，学生将通过亲身参与项目而直接受益。

The advent of Markov chain Monte Carlo (MCMC) methods is the most important development in statistical computing within the last fifteen years. While these algorithms have allowed statisticians to fit almost any conceivable model, statisticians have been (with notable exceptions) the only people who have been able to take full advantage of these estimation methods. This project provides a computational environment that puts MCMC methods in the hands of social scientists so that they too can use the power of these algorithms to fit innovative statistical models of their choosing. The project provides free, open-source, easy-to-use software for Bayesian inference that is geared towards the needs of social scientists. It also provides a documented development environment others can use to easily implement non-standard statistical models, and a mechanism for other researchers to distribute their own software with a consistent user interface and documentation. The project is based on a scientific approach to the provision and development of statistical software. The software development is cumulative and builds on the work of others; it is free, open-source, and cross-platform, thus allowing for widespread dissemination and an extremely quick development-release cycle. Further, because all users access to the underlying source code, it is straightforward to fix bugs and extend the software.The project puts powerful statistical methods into the hands of empirically-oriented social and behavioral scientists. As a result, it will improve the quality of empirical work done in these areas of study. More specifically, the project develops and provides computer software for statistical learning. The software makes use of state-of-the-art algorithms while remaining easy to use. The project also contains an instructional component that provides a suite of demonstration programs for use in undergraduate and graduate teaching. Furthermore, students will be directly benefited through first-hand involvement in the project.