Sequential Monte Carlo Methods in Practice
Sequential Monte Carlo Methods in Practice
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DOI：
10.1007/978-1-4757-3437-9
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发表时间：
2001
期刊：
影响因子：
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通讯作者：
A. Doucet;Nando de Freitas;N. Gordon
中科院分区：
文献类型：
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作者：
A. Doucet;Nando de Freitas;N. Gordon
蒙特卡罗方法正在彻底改变金融建模、目标跟踪和计算机视觉等领域的在线数据分析。这些方法，出现在引导过滤器，冷凝，最佳蒙特卡罗过滤器，粒子过滤器和适者生存的名称，已经有可能解决许多复杂的，非标准的问题，以前棘手的数值。这本书提出了这些技术的第一个全面的治疗，包括收敛结果和应用跟踪，指导，自动目标识别，飞机导航，机器人导航，计量经济学，金融建模，神经网络，最优控制，最优滤波，通信，强化学习，信号增强，模型平均和选择，计算机视觉，半导体设计，人口生物学，动态贝叶斯网络和时间序列分析。这将是非常有价值的学生，研究人员和数学家，谁拥有一些基本的概率知识。Arnaud Doucet获得了博士学位。1997年获得巴黎第十一大学奥赛学位。1998年至2000年，他在英国剑桥大学信号处理组进行研究。彼现为澳洲墨尔本大学电机工程系助理教授。他的研究兴趣包括贝叶斯统计，动态模型和蒙特卡罗方法。Nando de Freitas获得了博士学位。1999年毕业于剑桥大学信息工程专业。他目前是加州大学伯克利分校人工智能小组的研究助理。他的主要研究兴趣是贝叶斯统计以及在线和批量蒙特卡罗方法在机器学习中的应用。
Monte Carlo methods are revolutionising the on-line analysis of data in fields as diverse as financial modelling, target tracking and computer vision. These methods, appearing under the names of bootstrap filters, condensation, optimal Monte Carlo filters, particle filters and survial of the fittest, have made it possible to solve numerically many complex, non-standarard problems that were previously intractable. This book presents the first comprehensive treatment of these techniques, including convergence results and applications to tracking, guidance, automated target recognition, aircraft navigation, robot navigation, econometrics, financial modelling, neural networks, optimal control, optimal filtering, communications, reinforcement learning, signal enhancement, model averaging and selection, computer vision, semiconductor design, population biology, dynamic Bayesian networks, and time series analysis. This will be of great value to students, researchers and practicioners, who have some basic knowledge of probability. Arnaud Doucet received the Ph. D. degree from the University of Paris-XI Orsay in 1997. From 1998 to 2000, he conducted research at the Signal Processing Group of Cambridge University, UK. He is currently an assistant professor at the Department of Electrical Engineering of Melbourne University, Australia. His research interests include Bayesian statistics, dynamic models and Monte Carlo methods. Nando de Freitas obtained a Ph. D. degree in information engineering from Cambridge University in 1999. He is presently a research associate with the artificial intelligence group of the University of California at Berkeley. His main research interests are in Bayesian statistics and the application of on-line and batch Monte Carlo methods to machine learning.