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Neural network based approximations to posterior densities: a class of flexible sampling methods with applications to reduced rank models

机译：基于神经网络的后验密度近似值：一类灵活的采样方法，适用于降秩模型

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Hoogerheide L.F. (Lennart); Kaashoek J.F. (Johan); Dijk H.K. (Herman) van;
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年度 2004
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1. On the shape of posterior densities and credible sets in instrumental variable regression models with reduced rank: An application of flexible sampling methods using neural networks [J] . Lennart F. Hoogerheide, Johan F. Kaashoek, Herman K. van Dijk Journal of Econometrics . 2007,第1期

机译：秩降低的工具变量回归模型中后验密度和可信集的形状：神经网络灵活采样方法的应用
2. Neural networks for computing best rank-one approximations of tensors and its applications [J] . Che Maolin, Cichocki Andrzej, Wei Yimin Neurocomputing . 2017,第deca6期

机译：用于计算张量的最佳秩一近似的神经网络及其应用
3. Multilayer neural networks for reduced-rank approximation [J] . Diamantaras K.I., Sun-Yuan Kung IEEE Transactions on Neural Networks . 1994,第5期

机译：多层神经网络用于降秩逼近
4. Linear feedforward neural network classifiers and reduced-rank approximation [C] . De-Shuang Huang International conference on signal processing;ICSP '98 . 1998

机译：线性前馈神经网络分类器和降秩逼近
5. Asymptotic Posterior Approximation and Efficient MCMC Sampling for Generalized Linear Mixed Models [D] . Berman, Brandon. 2019

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6. Correlated Spatio-Temporal Data Collection in Wireless Sensor Networks Based on Low Rank Matrix Approximation and Optimized Node Sampling [O] . Xinglin Piao, Yongli Hu, Yanfeng Sun, 2014

机译：基于低秩矩阵逼近和优化节点采样的无线传感器网络相关时空数据收集
7. Neural network based approximations to posterior densities: a class of flexible sampling methods with applications to reduced rank models [O] . Hoogerheide L.F., Kaashoek J.F., Dijk H.K. van 100

机译：基于神经网络的后验密度近似：一类灵活的采样方法，适用于降低等级模型
8. Least-Squares Learning and Approximation of Posterior Probabilities on Classification Problems by Neural Network Models. [R] . Shoemaker, P. A., Carlin, M. J., Shimabukuro, R. L., 1991

机译：基于神经网络模型的分类问题最小二乘学习及后验概率逼近。

Neural network based approximations to posterior densities: a class of flexible sampling methods with applications to reduced rank models

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