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对贝叶斯联合贝叶斯网络推理的有效抽样
Efficient sampling for Bayesian inference of conjunctive Bayesian networks |
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| 课程网址: |
http://videolectures.net/mlsb2012_sakoparnig_sampling/
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| 主讲教师: |
Thomas Sakoparnig
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| 开课单位: |
苏黎世联邦理工学院
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| 开课时间: |
2012-10-23
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| 课程语种: |
英语
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| 中文简介: |
**动机：**癌症的发展是由有利突变的积累驱动的
随后克隆扩增的细胞含有这些突变，但突变的顺序
发生的事情仍然知之甚少。基因组测序和即将到来的洪水泛滥的进展
由大型癌症测序联盟产生的癌症基因组数据有望阐明
癌症进展。然而，需要新的计算方法来分析这些大的
数据集。\\
**结果：**我们提出了一个贝叶斯推断方案，用于联合贝叶斯网络，a
概率图模型，其中突变根据部分顺序约束累积
根据测量噪声观察和癌症基因型。我们开发了一个高效的MCMC
专门设计用于克服依赖性引起的局部最优的抽样方案
结构。我们展示了采样器相对于传统采样器的性能优势
关于模拟数据的方法，并展示了采用贝叶斯视角时的优势
重新分析癌症数据集，并将我们的结果与先前基于最大似然性的结果进行比较
接近。\\
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| 课程简介: |
**Motivation:** Cancer development is driven by the accumulation of advantageous mutations and subsequent clonal expansion of cells harbouring these mutations, but the order in which mutations occur remains poorly understood. Advances in genome sequencing and the soon-arriving flood of cancer genome data produced by large cancer sequencing consortia hold the promise to elucidate cancer progression. However, new computational methods are needed to analyse these large datasets.\\ **Results:** We present a Bayesian inference scheme for Conjunctive Bayesian Networks, a probabilistic graphical model in which mutations accumulate according to partial order constraints and cancer genotypes are observed subject to measurement noise. We develop an efficient MCMC sampling scheme specifically designed to overcome local optima induced by dependency structures. We demonstrate the performance advantage of our sampler over traditional approaches on simulated data and show the advantages of adopting a Bayesian perspective when reanalysing cancer datasets and comparing our results to previous maximum likelihood-based approaches.\\
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| 关 键 词: |
基因组测序; 癌症基因组; 贝叶斯网络
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| 课程来源: |
视频讲座网
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| 最后编审: |
2021-12-22：liyy
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| 阅读次数: |
72
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