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机译:从混合数据中潜在变量模型的可伸缩因果发现策略比较
Department of Computer Science, University of Pittsburgh, Pittsburgh, PA, USA;
Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA, USA;
UPMC Department of Medicine, Pittsburgh, PA, USA;
Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA;
Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA, USA;
Department of Computer Science, University of Pittsburgh, Pittsburgh, PA, USA;
Department of Philosophy, Carnegie Mellon University, Pittsburgh, PA, USA;
Department of Computer Science, University of Pittsburgh, Pittsburgh, PA, USA,Department of Computational and Systems Biology, University of Pittsburgh, Pittsburgh, PA, USA;
Causal inference; Latent variables; Mixed data; COPD; HIV;
机译:利用贝叶斯方法从实验和观测数据混合中发现潜在变量模型的原因
机译:使用混合效应潜在变量模型对潜在变量进行顺序分析:非信息性和信息性缺失数据的影响。
机译:使用基因表达数据和潜在变量模型作为二元分类器的多类肿瘤分类的两种输出编码策略的比较
机译:依赖模式在潜在变量模型因果发现中的应用
机译:分类和发现序列数据的潜在变量模型
机译:从混合数据中潜在变量模型的可伸缩因果发现策略比较
机译:潜在变量的偏微分方程模型的数据驱动的发现