摘要翻译:
提出了一种混合专家框架下隐变量选择的方法。我们引入了一种先验结构,其中信息取自一组独立的协变量。鲁棒的类隶属度预测器是使用正常的gamma先验来识别的。由此产生的模型设置被用于有限混合伯努利分布,以根据莫桑比克妇女关于艾滋病毒的信息来源寻找同质的妇女群体。完全贝叶斯推理是通过一个Gibbs采样器来实现的。
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英文标题:
《Bayesian shrinkage in mixture of experts models: Identifying robust
determinants of class membership》
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作者:
Gregor Zens
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最新提交年份:
2019
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分类信息:
一级分类:Economics 经济学
二级分类:Econometrics 计量经济学
分类描述:Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论,微观计量经济学,宏观计量经济学,通过新方法发现的经济关系的实证内容,统计推论应用于经济数据的方法论方面。
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英文摘要:
A method for implicit variable selection in mixture of experts frameworks is proposed. We introduce a prior structure where information is taken from a set of independent covariates. Robust class membership predictors are identified using a normal gamma prior. The resulting model setup is used in a finite mixture of Bernoulli distributions to find homogenous clusters of women in Mozambique based on their information sources on HIV. Fully Bayesian inference is carried out via the implementation of a Gibbs sampler.
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PDF链接:
https://arxiv.org/pdf/1809.04853