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元素的推断

Elements of Inference
课程网址: http://videolectures.net/clsp_jaakkola_inference/  
主讲教师: Tommi Jaakkola
开课单位: 麻省理工学院
开课时间: 2012-02-15
课程语种: 英语
中文简介:
大多数工程和科学问题涉及建模。我们需要推理计算从模型中绘制预测或从可用测量中估计它们。在许多情况下,推理计算可以仅在解码,传感器网络或建模生物系统中大致完成。在核心,推理任务将三种类型的问题联系在一起:计数(分区函数),几何(有效边缘)和不确定性(熵)。大多数近似推理方法可以被视为简化这种三向组合的不同方式。最近的大部分努力都花费在开发和理解分布式近似算法上,该算法减少到局部操作以解决全局问题。在本次演讲中,我将提供近似推理算法的优化视图,举例说明最新进展,并概述由于现代应用而出现的许多开放问题和连接。基于与Amir Globerson和David Sontag的联合工作。
课程简介: Most engineering and science problems involve modeling. We need inference calculations to draw predictions from the models or to estimate them from available measurements. In many cases the inference calculations can be done only approximately as in decoding, sensor networks, or in modeling biological systems. At the core, inference tasks tie together three types of problems: counting (partition function), geometry (valid marginals), and uncertainty (entropy). Most approximate inference methods can be viewed as different ways of simplifying this three-way combination. Much of recent effort has been spent on developing and understanding distributed approximation algorithms that reduce to local operations in an effort to solve a global problem. In this talk I will provide an optimization view of approximate inference algorithms, exemplify recent advances, and outline some of the many open problems and connections that are emerging due to modern applications. Based on joint work with Amir Globerson and David Sontag.
关 键 词: 元素的推断; 建模; 计数; 几何; 不确定性
课程来源: 视频讲座网
最后编审: 2020-06-24:yumf
阅读次数: 38