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自杀未遂的贝叶斯非参数模型

Bayesian Nonparametric Modeling of Suicide Attempts
课程网址: http://videolectures.net/machine_valera_attempts/  
主讲教师: Isabel Valera
开课单位: 卡洛斯三世马德里大学
开课时间: 2013-01-14
课程语种: 英语
中文简介:
关于酒精和相关病症的国家流行病学调查(NESARC)数据库包含大量关于美国人口代表性样本的生活方式,医疗状况,抑郁等的信息。在本文中,我们有兴趣寻找自杀企图背后隐藏的原因,我们建议使用基于印度自助餐过程(IBP)的非参数潜在模型对受试者进行建模。由于数据的性质,我们需要调整离散随机变量的观测模型。我们提出了一种生成模型,其中观察来自给定IBP矩阵的多项logit分布。使用拉普拉斯近似实现了有效的吉布斯采样器的实现,这允许我们整合出多项logit似然模型的加权因子。最后,在NESARC数据库上进行的实验表明,我们的模型正确地捕捉了一些模仿自杀企图的隐藏原因。
课程简介: The National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) database contains a large amount of information, regarding the way of life, medical conditions, depression, etc., of a representative sample of the U.S. population. In the present paper, we are interested in seeking the hidden causes behind the suicide attempts, for which we propose to model the subjects using a nonparametric latent model based on the Indian Buffet Process (IBP). Due to the nature of the data, we need to adapt the observation model for discrete random variables. We propose a generative model in which the observations are drawn from a multinomial-logit distribution given the IBP matrix. The implementation of an efficient Gibbs sampler is accomplished using the Laplace approximation, which allows us to integrate out the weighting factors of the multinomial-logit likelihood model. Finally, the experiments over the NESARC database show that our model properly captures some of the hidden causes that model suicide attempts.
关 键 词: 酒精; 自杀; 非参数潜在模型
课程来源: 视频讲座网
最后编审: 2019-05-15:lxf
阅读次数: 48