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部分观察扩散的一些最新进展，MCMC方法MCMC schemes for partially observed diffusions - Some recent advances |
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| 课程网址: | http://videolectures.net/aispds08_golighlty_mspod/ |
| 主讲教师: | Andrew Golightly |
| 开课单位: | 纽卡斯尔大学 |
| 开课时间: | 2008-08-05 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | It is well known that likelihood inference for arbitrary nonlinear diffusion processes observed at discrete times is problematic since closed form transition densities are rarely tractable. One widely used solution involves the introduction of latent data points between every pair of observations to allow a sufficiently accurate Euler-Maruyama approximation of the true transition densities. In recent literature, Markov chain Monte Carlo (MCMC) methods have been used to sample the posterior distribution of latent data and model parameters; however, naive schemes suffer from a mixing problem that worsens with the degree of augmentation. We will consider some recently developed MCMC schemes that are not adversely affected by the amount of augmentation. In particular, by sampling parameters conditional on a skeleton of the driving Brownian motion rather than the sample path, the mixing problem can be overcome. The methodology will be illustrated by estimating parameters governing the diffusion approximations of some interesting systems biological models. |
| 关 键 词: | 观察扩散; 最新进展 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-07：王勇彬(课程编辑志愿者) |
| 阅读次数: | 101 |