Improved evaluation of predictive probabilities in probit models with Gaussian process priors
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[1] Jian Cao,et al. Exploiting low-rank covariance structures for computing high-dimensional normal and Student-t probabilities , 2020, Statistics and Computing.
[2] C. Holmes,et al. Bayesian auxiliary variable models for binary and multinomial regression , 2006 .
[3] Ari Pakman,et al. Exact Hamiltonian Monte Carlo for Truncated Multivariate Gaussians , 2012, 1208.4118.
[4] Victor De Oliveira,et al. Bayesian Inference and Prediction of Gaussian Random Fields Based on Censored Data , 2005 .
[5] Alessio Benavoli,et al. Skew Gaussian processes for classification , 2020, Machine Learning.
[6] Daniele Durante,et al. Scalable and Accurate Variational Bayes for High-Dimensional Binary Regression Models , 2019 .
[7] David M. Blei,et al. Variational Inference: A Review for Statisticians , 2016, ArXiv.
[8] S. Chib,et al. Bayesian analysis of binary and polychotomous response data , 1993 .
[9] S. Ghosal,et al. Nonparametric binary regression using a Gaussian process prior , 2007 .
[10] Nicolas Chopin,et al. Fast simulation of truncated Gaussian distributions , 2011, Stat. Comput..
[11] Daniele Durante,et al. Conjugate Bayes for probit regression via unified skew-normal distributions , 2018, Biometrika.
[12] Daniele Durante,et al. A Class of Conjugate Priors for Multinomial Probit Models which Includes the Multivariate Normal One , 2020, J. Mach. Learn. Res..
[13] H. Chipman,et al. BART: Bayesian Additive Regression Trees , 2008, 0806.3286.
[14] David E. Keyes,et al. Hierarchical Decompositions for the Computation of High-Dimensional Multivariate Normal Probabilities , 2018 .
[15] Mark Girolami,et al. Variational Bayesian Multinomial Probit Regression with Gaussian Process Priors , 2006, Neural Computation.
[16] William C. Horrace,et al. Some results on the multivariate truncated normal distribution , 2005 .
[17] James Ridgway,et al. Leave Pima Indians alone: binary regression as a benchmark for Bayesian computation , 2015, 1506.08640.
[18] Luai M. Al-Hadhrami,et al. Potential of Establishment of Wind Farms in Western Province of Saudi Arabia , 2014 .
[19] Wei Chu,et al. Gaussian Processes for Ordinal Regression , 2005, J. Mach. Learn. Res..
[20] Jean-Michel Marin,et al. Mean-field variational approximate Bayesian inference for latent variable models , 2007, Comput. Stat. Data Anal..
[21] Andrew Gelman,et al. The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo , 2011, J. Mach. Learn. Res..
[22] P. McCullagh,et al. Generalized Linear Models , 1972, Predictive Analytics.
[23] A. Genz. Numerical Computation of Multivariate Normal Probabilities , 1992 .
[24] Adelchi Azzalini,et al. The Skew-Normal and Related Families , 2018 .
[25] Andreas Brezger,et al. Generalized structured additive regression based on Bayesian P-splines , 2006, Comput. Stat. Data Anal..
[26] M. Genton,et al. Current and Future Estimates of Wind Energy Potential Over Saudi Arabia , 2018, Journal of Geophysical Research: Atmospheres.
[27] Marc G. Genton,et al. Closing the gap between wind energy targets and implementation for emerging countries , 2020 .
[28] Aaron Smith,et al. MCMC for Imbalanced Categorical Data , 2016, Journal of the American Statistical Association.
[29] Z. Botev. The normal law under linear restrictions: simulation and estimation via minimax tilting , 2016, 1603.04166.
[30] Jian Cao,et al. Hierarchical-block conditioning approximations for high-dimensional multivariate normal probabilities , 2018, Stat. Comput..
[31] G. Powers,et al. A Description of the Advanced Research WRF Version 3 , 2008 .