Learning Complex Probabilistic Models from Data

真实的世界中的许多问题都涉及到复杂的、随机的和部分可观察的系统--例如，喷气发动机、人体和股票市场。 概率表示和推理技术，特别是概率（贝叶斯）网络的最新发展，导致了在工程，金融，医学，软件系统等方面的成功应用的快速增长。该项目涉及的技术的发展，自动化建设的复杂的概率网络直接从数据。首席研究员以前的工作表明，分布式的，基于梯度的方法可以用来学习具有隐变量的概率网络。这项工作正在扩展到处理时间过程的模型，连续和离散变量的混合模型，未知结构的模型，以及各种类型的先验知识可用的模型，从而为数据挖掘，预测，语音识别和计算机视觉等应用提供更强大的学习方法。由于概率网络可以为非常复杂的过程提供紧凑且易于理解的模型，因此该项目还将在当前数据分析工具无效的情况下为自动化科学调查提供新的工具-例如，在理解DNA病变导致肿瘤的复杂途径方面。

Many problems in the real world involve systems that are complex, stochastic, and partially observable -- for example, jet engines, the human body, and the stock market. Recent developments in probabilistic representation and reasoning technology, especially probabilistic (Bayesian) networks, have led to a rapid growth of successful applications in engineering, finance, medicine, software systems, etc. This project concerns the development of techniques for automated construction of complex probabilistic networks directly from data. Previous work by the principal investigator has shown that distributed, gradient-based methods can be used to learn probabilistic networks with hidden variables. This work is being extended to handle models of temporal processes, models with mixtures of continuous and discrete variables, models with unknown structure, and models for which various types of prior knowledge are available, thereby providing more powerful learning methods for applications such as data mining, forecasting, speech recognition, and computer vision. Because probabilistic networks can provide compact and easily understandable models of very complex processes, the project will also yield new tools for automated scientific investigation in cases where current data analysis tools are ineffective -- for example, in understanding the complex pathways by which DNA lesions lead to tumors.