Aki Vehtari

Simo Särkkä, Arno Solin, Aapo Nummenmaa, Aki Vehtari, Toni Auranen, Simo Vanni, Fa-Hsuan Lin: Dynamic retrospective filtering of physiological noise in BOLD fMRI: DRIFTER. NeuroImage 60(2): 1517-1527 (2012)

Jarno Vanhatalo, Aki Vehtari: Speeding up the binary Gaussian process classification. CoRR abs/1203.3524 (2012)

Jarno Vanhatalo, Aki Vehtari: Modelling local and global phenomena with sparse Gaussian processes. CoRR abs/1206.3290 (2012)

Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen, Aki Vehtari: Bayesian Modeling with Gaussian Processes using the MATLAB Toolbox GPstuff (v3.3). CoRR abs/1206.5754 (2012)

Pasi Jylänki, Jarno Vanhatalo, Aki Vehtari: Robust Gaussian Process Regression with a Student-t Likelihood. Journal of Machine Learning Research 12: 3227-3257 (2011)

Jaakko Riihimäki, Aki Vehtari: Gaussian processes with monotonicity information. Journal of Machine Learning Research - Proceedings Track 9: 645-652 (2010)

Elina Parviainen, Aki Vehtari: Explaining Classification by Finding Response-Related Subgroups in Data. SNPD 2010: 69-75

Jarno Vanhatalo, Aki Vehtari: Speeding up the binary Gaussian process classification. UAI 2010: 623-631

Elina Parviainen, Aki Vehtari: Features and Metric from a Classifier Improve Visualizations with Dimension Reduction. ICANN (2) 2009: 225-234

Jarno Vanhatalo, Pasi Jylänki, Aki Vehtari: Gaussian process regression with Student-t likelihood. NIPS 2009: 1910-1918

Jarno Vanhatalo, Aki Vehtari: Modelling local and global phenomena with sparse Gaussian processes. UAI 2008: 571-578

Aki Vehtari, Ville-Petteri Mäkinen, Pasi Soininen, Petri Ingman, Sanna M. Mäkelä, Markku J. Savolainen, Minna L. Hannuksela, Kimmo Kaski, Mika Ala-Korpela: A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in ¹H NMR metabonomic data. BMC Bioinformatics 8(S-2) (2007)

Simo Särkkä, Aki Vehtari, Jouko Lampinen: CATS benchmark time series prediction by Kalman smoother with cross-validated noise density. Neurocomputing 70(13-15): 2331-2341 (2007)

Simo Särkkä, Aki Vehtari, Jouko Lampinen: Rao-Blackwellized particle filter for multiple target tracking. Information Fusion 8(1): 2-15 (2007)

Jarno Vanhatalo, Aki Vehtari: Sparse Log Gaussian Processes via MCMC for Spatial Epidemiology. Journal of Machine Learning Research - Proceedings Track 1: 73-89 (2007)

Marko Sysi-Aho, Aki Vehtari, Vidya R. Velagapudi, Jukka Westerbacka, Laxman Yetukuri, Robert Bergholm, Marja-Riitta Taskinen, Hannele Yki-Järvinen, Matej Oresic: Exploring the lipoprotein composition using Bayesian regression on serum lipidomic profiles. ISMB/ECCB (Supplement of Bioinformatics) 2007: 519-528

Ilkka Kalliomäki, Aki Vehtari, Jouko Lampinen: Shape analysis of concrete aggregates for statistical quality modeling. Mach. Vis. Appl. 16(3): 197-201 (2005)

Aki Vehtari, Jouko Lampinen: Bayesian Model Assessment and Comparison Using Cross-Validation Predictive Densities. Neural Computation 14(10): 2439-2468 (2002)

Jouko Lampinen, Aki Vehtari: Bayesian approach for neural networks--review and case studies. Neural Networks 14(3): 257-274 (2001)

Aki Vehtari, Jouko Lampinen: Bayesian MLP neural networks for image analysis. Pattern Recognition Letters 21(13-14): 1183-1191 (2000)

Aki Vehtari, Simo Särkkä, Jouko Lampinen: On MCMC Sampling in Bayesian MLP Neural Networks. IJCNN (1) 2000: 317-322