Probabilistic Modeling of Heteroscedastic Laboratory Experiments Using Gaussian Process Regression
暂无分享,去创建一个
[1] Jouko Lampinen,et al. Bayesian approach for neural networks--review and case studies , 2001, Neural Networks.
[2] I Cheng Yeh. Estimating Distribution of Concrete Strength Using Quantile Regression Neural Networks , 2014 .
[3] Karthick Thiyagarajan,et al. Analytical Model and Data-driven Approach for Concrete Moisture Prediction , 2016 .
[4] Takafumi Noguchi,et al. Bayesian Statistical Framework to Construct Probabilistic Models for the Elastic Modulus of Concrete , 2007 .
[5] Carl E. Rasmussen,et al. Gaussian Processes for Machine Learning (GPML) Toolbox , 2010, J. Mach. Learn. Res..
[6] Reginald DesRoches,et al. Statistical models for shear strength of RC beam‐column joints using machine‐learning techniques , 2014 .
[7] Jianren Zhang,et al. Bridge Remaining Strength Prediction Integrated with Bayesian Network and In Situ Load Testing , 2014 .
[8] Paul W. Goldberg,et al. Regression with Input-dependent Noise: A Gaussian Process Treatment , 1997, NIPS.
[9] Marek Słoński,et al. A comparison of model selection methods for compressive strength prediction of high-performance concrete using neural networks , 2010 .
[10] Aki Vehtari,et al. Expectation propagation for nonstationary heteroscedastic Gaussian process regression , 2014, 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP).
[11] Wolfram Burgard,et al. Most likely heteroscedastic Gaussian process regression , 2007, ICML '07.
[12] Iain Murray. Introduction To Gaussian Processes , 2008 .
[13] Radford M. Neal,et al. Gaussian Process Regression with Heteroscedastic or Non-Gaussian Residuals , 2012, ArXiv.
[14] Marek Słoński,et al. Bayesian neural networks and Gaussian processes in identification of concrete properties , 2011 .
[15] Carl E. Rasmussen,et al. Gaussian processes for machine learning , 2005, Adaptive computation and machine learning.
[16] Chunyi Wang,et al. Gaussian Process Regression with Heteroscedastic Residuals and Fast MCMC Methods , 2014 .
[17] Venkat Venkatasubramanian,et al. High fidelity mathematical model building with experimental data: A Bayesian approach , 2008, Comput. Chem. Eng..
[18] Aki Vehtari,et al. Bayesian Modeling with Gaussian Processes using the GPstuff Toolbox , 2012, 1206.5754.
[19] Miguel Lázaro-Gredilla,et al. Variational Heteroscedastic Gaussian Process Regression , 2011, ICML.
[20] Priyanka Aggarwal,et al. Bayes Prediction for a Heteroscedastic Regression Superpopulation Model Using Balanced Loss Function , 2007 .
[21] Mahesh Pal,et al. Modeling Pile Capacity Using Support Vector Machines and Generalized Regression Neural Network , 2008 .
[22] Rafat Siddique,et al. Modeling properties of self-compacting concrete: support vector machines approach , 2008 .
[23] Alexander J. Smola,et al. Heteroscedastic Gaussian process regression , 2005, ICML.
[24] Maxime Hubert,et al. Influence of fiber content and reinforcement ratio on the water permeability of reinforced concrete , 2015 .
[25] Jinquan Zhong,et al. Probabilistic Seismic Demand Models and Fragility Estimates for Reinforced Concrete Bridges with Two-Column Bents , 2008 .
[26] Armen Der Kiureghian,et al. Probabilistic Capacity Models and Fragility Estimates for Reinforced Concrete Columns based on Experimental Observations , 2002 .