CIF: Small: Message Passing Networks

这个项目的目标是在概率图形模型领域为消息传递算法开发基础。该项目将侧重于图形模型中的三类问题：(A)从观测数据学习图形模型，(B)计算图形模型的模式，以及(C)计算图形模型中变量的边际。该项目将确定流行的信念传播算法在所有三个问题上的优势和局限性。此外，该项目将通过开发图形模型的几何结构来开发具有可证明性能保证的新型高效消息传递算法。概率图形模型已成为在广泛的应用中简洁地表示不确定性的标准方法：通信和信号处理、计算、视觉和图像处理、生物信息学、自然语言处理等。在这些应用中面临的问题在很大程度上是本项目的研究成果，该项目将被归入PI讲授的名为《推理算法(6.438)》的课程中。这门课程是一门很受欢迎的入门级研究生课程，它还通过麻省理工学院开放式课程在线传播材料，并在未来可能探索通过MITx/EdX更广泛传播的可能性。

The goal of this project is to develop foundations for message-passing algorithms, in the realm of probabilistic graphical models. The project will focus on three classes of problems in graphical models: (a) learning graphical models from observed data, (b) computing the mode of a graphical model, and (c) computing marginals of variables in a graphical model. The project will identify the strengths and limitations of the popular belief propagation algorithm for all three problems. In addition, the project will develop new class of efficient message-passing algorithms with provable performance guarantees by means of exploiting the geometry of the graphical model.Probabilistic graphical models have become a standard way to represent uncertainty succinctly in a wide variety of applications: communication and signal processing, computation, vision and image processing, bioinformatics, natural language processing, and more. The problems faced in these applications are largely The research outcome of this project will be folded in the course titled "Algorithms for Inference (6.438)" taught by the PI. The course is a popular entry level graduate course which also disseminates material online through MIT Open CourseWare and going forward, it may explore possibility of wider dissemination through MITx/EdX.