图 书 馆
统计推断、模型选择和决策的蒙特卡罗模拟Monte Carlo Simulation for Statistical Inference, Model Selection and Decision Making |
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| 课程网址: | http://videolectures.net/mlss08au_freitas_asm/ |
| 主讲教师: | Nando de Freitas |
| 开课单位: | 不列颠哥伦比亚大学 |
| 开课时间: | 2008-03-13 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | **The first part** of his course will consist of two presentations. In the first presentation, he will introduce fundamentals of Monte Carlo simulation for statistical inference, with emphasis on algorithms such as importance sampling, particle filtering and smoothing for dynamic models, Markov chain Monte Carlo, Gibbs and Metropolis-Hastings, blocking and mixtures of MCMC kernels, Monte Carlo EM, sequential Monte Carlo for static models, auxiliary variable methods (Swedsen-Wang, hybrid Monte Carlo and slice sampling), and adaptive MCMC. The algorithms will be illustrated with several examples: image tracking, robotics, image annotation, probabilistic graphical models, and music analysis. \\ **The second presentation** will target model selection and decision making problems. He will describe the reversible-jump MCMC algorithm and illustrate it with application to simple mixture models and nonlinear regression with an unknown number of basis functions. He will show how to apply this algorithm to general Markov decision processes (MDPs). The course will also cover other Monte Carlo simulation methods for partially observed Markov decision processes (POMDPs) using policy gradients, common random number generation, and active exploration with Gaussian processes. An outline to some applications of these methods to robotics and the design of computer game architectures will be given. The presentation will end with the problem of Monte Carlo simulation for Bayesian nonlinear experimental design, with application to financial modeling, robot exploration, drug treatments, dynamic sensor networks, optimal measurement and active vision. |
| 关 键 词: | 统计推断; 蒙特卡罗模拟; 可逆跳跃 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-07-24：cwx |
| 阅读次数: | 46 |