RI: Medium: Approximation Algorithms for Probabilistic Graphical Models with Constraints

这个项目的目标是为概率图形模型创建下一代近似推理技术和算法。概率图形模型在整个科学和工程中被用来解决难题，包括自动推理和决策、计算生物学和遗传学、计算机视觉、数据挖掘和社会网络分析。然而，这些现实世界的问题现在是如此之大，以至于大多数现有技术的性能参差不齐，因为它们通常在一些问题上工作得很好，而不是另一些问题，并且通常需要一组必须在很少指导或自动化的情况下进行的选择和定制。该项目汇集了独立但互补的研究流，以开发新的算法来管理包含概率和确定性关系以及基于图形和上下文敏感关系的混合的模型。该项目旨在通过开发新的图形模型近似推理技术，例如通过利用大多数抽样技术在很大程度上忽略的图形模型的丰富结构，来促进存在确定性约束的概率推理的技术水平。该项目旨在通过改进近似推理的采样和消息传递算法，并开发利用各自优势的混合方法，来创建改进的概率图形模型框架。这些框架将被用来为选择参数提供自动指导，以优化复杂性和准确性之间的内在权衡，并对结果和准确性提供有意义的界限。该项目将利用其研究成果来改善本科生和研究生的教育，例如，通过开发一门新的本科课程的图形模型，以及通过在网上张贴课程材料。此外，该项目还将在网络上发布开放源代码。

The goal of this project is to create the next generation of approximate inference techniques and algorithms for probabilistic graphical models. Probabilistic graphical models are employed throughout science and engineering to solve difficult problems, including automated reasoning and decision making, computational biology and genetics, computer vision, data mining, and social network analysis. However, these real-world problems are now of such considerable size that most existing techniques are uneven in their performance in that they typically work well on some problems and not others, and often require sets of choices and customizations that must be made with little guidance or automation.This project brings together separate but complementary streams of research to develop new algorithms to manage models containing mixtures of probabilistic and deterministic relations and mixtures of graph-based and context-sensitive relationships. This project aims to advance the state of the art of probabilistic reasoning in the presence of deterministic constraints by developing new approximate inference techniques for graphical models, for instance, by exploiting the rich structure of graphical models that is largely neglected by most sampling techniques. This project aims to create improved frameworks for probabilistic graphical models by improving both sampling and message-passing algorithms for approximate inference and developing hybrid approaches that exploit the advantages of each. The frameworks will be used to provide automated guidance for selecting parameters to optimize the inherent tradeoffs between complexity and accuracy as well as provide meaningful bounds on results and accuracy. This project will use the fruits of its research to improve education, both at the undergraduate and graduate level, for instance by developing a new undergraduate course in graphical models, and by posting course materials online. In addition, the project will post open source code on the web.