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Expectation propagation as a way of life: A framework for Bayesian inference on partitioned data

Mar 10, 2018
Aki Vehtari, Andrew Gelman, Tuomas Sivula, Pasi Jylänki, Dustin Tran, Swupnil Sahai, Paul Blomstedt, John P. Cunningham, David Schiminovich, Christian Robert

* Updated. 31 pages (+ appendix) 

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Regularizing Solutions to the MEG Inverse Problem Using Space-Time Separable Covariance Functions

Apr 17, 2016
Arno Solin, Pasi Jylänki, Jaakko Kauramäki, Tom Heskes, Marcel A. J. van Gerven, Simo Särkkä

* 25 pages, 7 figures 

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Bayesian Modeling with Gaussian Processes using the GPstuff Toolbox

Jul 15, 2015
Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, Aki Vehtari

* - Updated according to GPstuff 4.6. Added, e.g., Pareto smoothed importance sampling 

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Approximate Inference for Nonstationary Heteroscedastic Gaussian process Regression

Apr 22, 2014
Ville Tolvanen, Pasi Jylänki, Aki Vehtari

* 2014 IEEE International Workshop on Machine Learning for Signal Processing (MLSP) 

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Expectation Propagation for Neural Networks with Sparsity-promoting Priors

Mar 27, 2013
Pasi Jylänki, Aapo Nummenmaa, Aki Vehtari

* Journal of Machine Learning Research, 15(May): 1849-1901, 2014 

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Nested Expectation Propagation for Gaussian Process Classification with a Multinomial Probit Likelihood

Jul 16, 2012
Jaakko Riihimäki, Pasi Jylänki, Aki Vehtari

* Journal of Machine Learning Research 14 (2013) 75-109 

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Gaussian Process Regression with a Student-t Likelihood

Jun 22, 2011
Pasi Jylänki, Jarno Vanhatalo, Aki Vehtari

* Journal of Machine Learning Research 12 (2011) 3227-3257 

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