图 书 馆
mc4：一种大样本网络推理的回火算法MC4: A Tempering Algorithm for Large-sample Network Inference |
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| 课程网址: | http://videolectures.net/prib2010_barker_mtal/ |
| 主讲教师: | Daniel James Barker |
| 开课单位: | 华威大学 |
| 开课时间: | 2010-10-14 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Bayesian networks and their variants are widely used for modelling gene regulatory and protein signalling networks. In many settings, it is the underlying network structure itself that is the object of inference. Within a Bayesian framework inferences regarding network structure are made via a posterior probability distribution over graphs. However, in practical problems, the space of graphs is usually too large to permit exact inference, motivating the use of approximate approaches. An MCMC-based algorithm known as MC3 is widely used for network inference in this setting. We argue that recent trends towards larger sample size datasets, while otherwise advantageous, can, for reasons related to concentration of posterior mass, render inference by MC3 harder. We therefore exploit an approach known as parallel tempering to put forward an algorithm for network inference which we call MC4. We show empirical results on both synthetic and proteomic data which highlight the ability of MC4 to converge faster and thereby yield demonstrably accurate results, even in challenging settings where MC3 fails. |
| 关 键 词: | 贝叶斯网络; 基因调控; 蛋白质信号 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-09-14：lxf |
| 阅读次数: | 62 |