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基于扩散近似的系统生物模型贝叶斯推理
Bayesian Inference for Systems Biological Models via a Diffusion Approximation |
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| 课程网址: |
http://videolectures.net/pesb07_golightly_bif/
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| 主讲教师: |
Andrew Golightly
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| 开课单位: |
纽卡斯尔大学
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| 开课时间: |
2007-04-04
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| 课程语种: |
英语
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| 中文简介: |
随着后基因组生物学变得更具预测性，推断生化网络的速率参数（称为逆向工程）的能力将变得越来越重要。一种方法是通过扩散近似替换基础模型，并且使用可能出错的离散时间（并且通常是不完整的）数据来识别模型。不幸的是，基于似然的推断可能是有问题的，因为非线性扩散的封闭形式转换密度很少可用。一种广泛使用的解决方案涉及在每对观察之间引入潜在数据点，以允许Euler Maruyama近似真实的过渡密度变得准确。然后可以使用马尔可夫链蒙特卡罗（MCMC）方法对潜在数据和模型参数的后验分布进行采样;然而，天真的方案遭受混合问题，随着增强程度而恶化。因此实施重新参数化以克服该困难，并且该方法应用于简单的原核自身调节基因网络。与Darren J. Wilkinson合作
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| 课程简介: |
As post-genomic biology becomes more predictive, the ability to infer rate parameters (known as reverse-engineering) of biochemical networks will become increasingly important. One approach is to replace the underlying model by a diffusion approximation and the model is identified using discrete-time (and often incomplete) data that is subject to error. Unfortunately, likelihood based inference can be problematic as closed form transition densities of nonlinear diffusions are rarely available. A widely used solution involves the introduction of latent data points between every pair of observations to allow an Euler-Maruyama approximation of the true transition densities to become accurate. Markov chain Monte Carlo (MCMC) methods can then be 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. A reparameterisation is therefore implemented to overcome this difficulty and the methodology is applied to a simple prokaryotic auto-regulatory gene network. Joint work with Darren J. Wilkinson
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| 关 键 词: |
后基因组; 生化网络; 非线性扩散
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| 课程来源: |
视频讲座网
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| 最后编审: |
2019-09-13：lxf
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| 阅读次数: |
60
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