jmlr jmlr2013 jmlr2013-45 jmlr2013-45-reference knowledge-graph by maker-knowledge-mining
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Author: Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, Aki Vehtari
Abstract: The GPstuff toolbox is a versatile collection of Gaussian process models and computational tools required for Bayesian inference. The tools include, among others, various inference methods, sparse approximations and model assessment methods. Keywords: Gaussian process, Bayesian hierarchical model, nonparametric Bayes
Timothy A. Davis. Algorithm 849: A concise sparse Cholesky factorization package. ACM Trans. Math. Softw., 31:587–591, 2005. ISSN 0098-3500. Pasi Jyl¨ nki, Jarno Vanhatalo, and Aki Vehtari. Robust Gaussian process regression with a Student-t a likelihood. Journal of Machine Learning Research, 12:3227–3257, 2011. Ian T. Nabney. NETLAB: Algorithms for Pattern Recognition. Springer, 2001. Radford Neal. Regression and classification using Gaussian process priors. In J. M. Bernardo, J. O. Berger, A. P. David, and A. P. M. Smith, editors, Bayesian Statistics 6, pages 475–501. Oxford University Press, 1998. Joaquin Qui˜ onero-Candela and Carl Edward Rasmussen. A unifying view of sparse approximate n Gaussian process regression. Journal of Machine Learning Research, 6(3):1939–1959, 2005. Carl Edward Rasmussen and Hannes Nickisch. Gaussian processes for machine learning (GPML) toolbox. Journal of Machine Learning Research, 11:3011–3015, 2010. Carl Edward Rasmussen and Christopher K. I. Williams. Gaussian Processes for Machine Learning. The MIT Press, 2006. Jaakko Riihim¨ ki and Aki Vehtari. Laplace approximation for logistic Gaussian process density a estimation. ArXiv e-prints, (1211.0174), 2012. URL http://arxiv.org/abs/1211.0174. Jaakko Riihim¨ ki, Pasi Jyl¨ nki, and Aki Vehtari. Nested expectation propagation for Gaussian proa a cess classification with a multinomial probit likelihood. Journal of Machine Learning Research, 14:75–109, 2013. H˚ vard Rue, Sara Martino, and Nicolas Chopin. Approximate Bayesian inference for latent Gausa sian models by using integrated nested Laplace approximations. Journal of the Royal Statistical Society B, 71(2):1–35, 2009. Michalis K. Titsias. Variational learning of inducing variables in sparse Gaussian processes. JMLR Workshop and Conference Proceedings, 5:567–574, 2009. Jarno Vanhatalo and Aki Vehtari. Modelling local and global phenomena with sparse Gaussian processes. In David A. McAllester and Petri Myllym¨ ki, editors, Proceedings of the 24th Conference a on Uncertainty in Artificial Intelligence, pages 571–578, 2008. Jarno Vanhatalo, Ville Pietil¨ inen, and Aki Vehtari. Approximate inference for disease mapping a with sparse Gaussian processes. Statistics in Medicine, 29(15):1580–1607, 2010. Jarno Vanhatalo, Jaakko Riihim¨ ki, Jouni Hartikainen, Pasi Jyl¨ nki, Ville Tolvanen, and Aki Vea a htari. Bayesian modeling with Gaussian processes using the GPstuff toolbox. ArXiv e-prints, (1206.5754), 2013. URL http://arxiv.org/abs/1206.5754. Aki Vehtari and Janne Ojanen. A survey of Bayesian predictive methods for model assessment, selection and comparison. Statistics Surveys, 6:142–228, 2012. 1179