面向海量高维数据的可深度结合的贝叶斯网学习与推理新方法研究

项目名称： 面向海量高维数据的可深度结合的贝叶斯网学习与推理新方法研究

项目编号： No.61502198

项目类型： 青年科学基金项目

立项/批准年度： 2016

项目学科： 其他

项目作者： 朱允刚

作者单位： 吉林大学

项目金额： 20万元

中文摘要： 贝叶斯网是一种以概率论为基础、描述随机变量间因果关系，并能高效表示全概率分布的图模型，已广泛应用于众多领域。随着大数据时代的到来，人类获得的数据往往存在海量、高维等特点，面向海量、高维数据的复杂贝叶斯网的学习与推理研究还存在如下主要问题：高维数据下传统学习与推理方法复杂度过高；已有学习方法难以有效处理目标概率分布动态变化的情况，且难以结合先验知识；已有方法往往将学习和推理独立开来，可能导致耗费很大计算量却学到推理效率很低的模型。为此，本项目拟围绕以上问题开展研究，提出贝叶斯网自适应增量学习方法、利用先验知识或依赖分析约束搜索空间的方法、基于分治策略学习高维贝叶斯网的方法、面向复杂贝叶斯网的离线和在线精确推理方法，并提出将学习与推理深度结合的方法，使学习过程能够学到易于高效推理的贝叶斯网。以期大幅提高复杂贝叶斯网的学习与推理效率。本项目的实施对深化、拓展贝叶斯网的理论与应用研究具有重要意义。

中文关键词： 贝叶斯网；概率图模型；贝叶斯网学习；贝叶斯网推理；统计关系学习

英文摘要： Bayesian network is a graphical model that based on probability theory,it describes the causal relationships between random variables and represents joint probability distribution efficiently.With the advent of the big data age, the data human acquired are often massive and high dimensional. There are still following problems about learning and inference for complex Bayesian network from massive and high dimensional data: the complexity of traditional learning and inference approaches for high dimensional Bayesian network is too high;existing Bayesian network learning approaches are difficult to handle the situation of the target probability distribution drift over time, and they are difficult to combine prior knowledge; existing researches always separates learning and inference that may result in cost lots of computation to learn a Bayesian network with low inference efficiency .Therefore, this project intends to carry out thorough research on the aforementioned issues, aiming to propose adaptive incremental learning approach for learning Bayesian network, combine prior knowledge or dependency analysis to constrain the search space for leaning Bayesian network, and learn high dimensional Bayesian network based on the strategy of divide and conquer, and propose offline and online exact inference approach for complex Bayesian network; then propose the approach for deep combination of learning and inference in order to learn a Bayesian network with high inference efficiency. The implementation of this project will deepen, expand and promote the research and application of Bayesian network.

英文关键词： Bayesian Network;Probabilistic Graphical Models;Bayesian Network Learning;Bayesian Network Inference;Statistical Relational Learning