0


多通道与多选择,滑膜控制,下一步是什么?

MCMC, SMC,... What next ?
课程网址: http://videolectures.net/acs07_moulines_mcm/  
主讲教师: Eric Moulines
开课单位: 巴黎ENST公司
开课时间: 2007-12-17
课程语种: 英语
中文简介:
蒙特卡罗方法最初是为统计物理中的科学计算而开发的,在计算机的早期阶段。由于计算机技术的快速发展以及对处理大数据集和复杂系统的需要,在过去的20年里,科学界对蒙特卡罗方法的兴趣激增。研究人员范围从计算生物学家到信号& &;图像处理工程师和金融计量学家现在将蒙特卡洛技术视为推理的基本工具。除了使用流行的马尔可夫链蒙特卡罗策略和它的自适应变体之外,各种顺序蒙特卡罗策略最近也出现了,产生了大量新颖有效的推理和优化工具。在这次演讲中,我们将介绍我们所认为的“最先进的技术”;在蒙特卡罗模拟中进行推理,并将努力识别下一个挑战。
课程简介: The Monte Carlo method was initially developed for scientific computing in statistical physics during the early days of the computers. Due to the rapid progress in computer technology and the need for handling large datasets and complex systems, the past two decades have witnessed a strong surge of interest in Monte Carlo methods from the scientific community. Researchers ranging from computational biologist to signal \& image processing engineers and to financial econometricians now view Monte Carlo techniques as essential tools for inference. Besides using the popular Markov chain Monte Carlo strategies and adaptive variants of it, various sequential Monte Carlo strategies have recently appeared on the scene, resulting in a wealth of novel and effective inferential and optimization tools. In this talk, we will present what we believe to be the "state-of-the art" in Monte-Carlo simulations for inference and will try to identify the next challenges.
关 键 词: 蒙特卡洛方法; 统计物理; 科学计算
课程来源: 视频讲座网
最后编审: 2020-07-29:yumf
阅读次数: 61