Nested Kriging predictions for datasets with a large number of observations
暂无分享,去创建一个
François Bachoc | Didier Rullière | Nicolas Durrande | Clément Chevalier | N. Durrande | F. Bachoc | C. Chevalier | D. Rullière
[1] Hao Wang,et al. Optimally Weighted Cluster Kriging for Big Data Regression , 2015, IDA.
[2] François Bachoc,et al. Cross Validation and Maximum Likelihood estimations of hyper-parameters of Gaussian processes with model misspecification , 2013, Comput. Stat. Data Anal..
[3] Ashwini Maurya,et al. A Well-Conditioned and Sparse Estimation of Covariance and Inverse Covariance Matrices Using a Joint Penalty , 2014, J. Mach. Learn. Res..
[4] Robert L. Winkler,et al. The Consensus of Subjective Probability Distributions , 1968 .
[5] R. L. Winkler. Combining Probability Distributions from Dependent Information Sources , 1981 .
[6] Jianhua Z. Huang,et al. Full-scale approximations of spatio-temporal covariance models for large datasets , 2014 .
[7] Roger Woodard,et al. Interpolation of Spatial Data: Some Theory for Kriging , 1999, Technometrics.
[8] J. Chaboche,et al. Mechanics of Solid Materials , 1990 .
[9] Michael A. Osborne,et al. Blitzkriging: Kronecker-structured Stochastic Gaussian Processes , 2015, 1510.07965.
[10] Lyle H. Ungar,et al. Modeling Probability Forecasts via Information Diversity , 2014 .
[11] David J. Fleet,et al. Generalized Product of Experts for Automatic and Principled Fusion of Gaussian Process Predictions , 2014, ArXiv.
[12] N. Cressie,et al. A Fast, Optimal Spatial-Prediction Method for Massive Datasets , 2005 .
[13] Michael L. Stein,et al. Limitations on low rank approximations for covariance matrices of spatial data , 2014 .
[14] François Laviolette,et al. Domain-Adversarial Training of Neural Networks , 2015, J. Mach. Learn. Res..
[15] T. Gneiting,et al. Combining probability forecasts , 2010 .
[16] Marc Peter Deisenroth,et al. Distributed Gaussian Processes , 2015, ICML.
[17] Di Wu,et al. A k-d tree-based algorithm to parallelize Kriging interpolation of big spatial data , 2015 .
[18] H. Künsch. Gaussian Markov random fields , 1979 .
[19] A. Azzouz. 2011 , 2020, City.
[20] Edward I. George,et al. Bayes and big data: the consensus Monte Carlo algorithm , 2016, Big Data and Information Theory.
[21] Sonja Kuhnt,et al. Design and analysis of computer experiments , 2010 .
[22] Thomas J. Santner,et al. The Design and Analysis of Computer Experiments , 2003, Springer Series in Statistics.
[23] Neil D. Lawrence,et al. Gaussian Processes for Big Data , 2013, UAI.
[24] Stephen J. Roberts,et al. String and Membrane Gaussian Processes , 2015, J. Mach. Learn. Res..
[25] A. Gelfand,et al. Adaptive Gaussian predictive process models for large spatial datasets , 2011, Environmetrics.
[26] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[27] Florence March,et al. 2016 , 2016, Affair of the Heart.
[28] Volker Tresp,et al. A Bayesian Committee Machine , 2000, Neural Computation.
[29] Christian Genest,et al. Combining Probability Distributions: A Critique and an Annotated Bibliography , 1986 .
[30] Matthias Katzfuss,et al. Bayesian nonstationary spatial modeling for very large datasets , 2012, 1204.2098.
[31] Franccois Bachoc,et al. Some properties of nested Kriging predictors , 2017, 1707.05708.
[32] Shalabh Bhatnagar,et al. Stochastic Recursive Algorithms for Optimization , 2012 .
[33] Geoffrey E. Hinton. Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.
[34] Yves Deville,et al. DiceKriging, DiceOptim: Two R Packages for the Analysis of Computer Experiments by Kriging-Based Metamodeling and Optimization , 2012 .
[35] Leonhard Held,et al. Gaussian Markov Random Fields: Theory and Applications , 2005 .
[36] Gene H. Golub,et al. Matrix computations , 1983 .
[37] G. Wahba. Spline models for observational data , 1990 .