Bayesian methodology group's publications (until 2014)

This page lists the publications of the Bayesian methodology research group from the former department of Biomedical Engineering and Computational Science (BECS), Aalto University. The group is now part of the Probabilistic Machine Learning group at the department of Computer Science, Aalto University. For publications from 2015 onwards, see the new publication list.

2014

• Andrew Gelman, Aki Vehtari, Pasi Jylänki, Christian Robert, Nicolas Chopin and John P. Cunningham (2014). Expectation propagation as a way of life. arXiv:1412.4869. PDF.
• Ville Tolvanen, Pasi Jylänki and Aki Vehtari (2014). Expectation propagation for nonstationary heteroscedastic Gaussian process regression. In Machine Learning for Signal Processing (MLSP), 2014 IEEE International Workshop on, DOI:10.1109/MLSP.2014.6958906. Online. Preprint. Code available in GPstuff toolbox.
• Tomi Peltola, Aki S. Havulinna, Veikko Salomaa and Aki Vehtari (2014). Hierarchical Bayesian survival analysis and projective covariate selection in cardiovascular event risk prediction. In Laskey, K. B., Jones, J. and Almond, R. (eds.) Proceedings of Eleventh UAI Bayesian Bayesian Modeling Applications Workshop (BMAW 2014), CEUR Workshop Proceedings Vol-1218, 79-88. Online. Code.
• Jaakko Riihimäki and Aki Vehtari (2014). Laplace approximation for logistic Gaussian process density estimation and regression. Bayesian analysis, 9(2):425-448. Online 3 February, 2014. Code available in GPstuff toolbox.
• Tomi Peltola, Pasi Jylänki and Aki Vehtari (2014). Expectation propagation for likelihoods depending on an inner product of two multivariate random variables. Journal of Machine Learning Research: Workshop and Conference Proceedings (AISTATS 2014 Proceedings), 33:769-777. Online. Code.
• Heikki Joensuu, Peter Reichardt, Mikael Eriksson, Kirsten Sundby Hall and Aki Vehtari (2014). Gastrointestinal stromal tumor: A method for optimizing the timing of CT scans in the follow-up of cancer patients. Radiology, 271(1):96-106. Online 18 November, 2013. Preprint of the statistical appendix. Related poster presented at The Third Workshop on Bayesian Inference for Latent Gaussian Models with Applications. Highlighted in "This Month in Radiology".
• Pasi Jylänki, Aapo Nummenmaa and Aki Vehtari (2014). Expectation propagation for neural networks with sparsity-promoting priors. Journal of Machine Learning Research, 15(May):1849-1901. Online. Code.
• Aki Vehtari, Karita Reijonsaari, Olli-Pekka Kahilakoski, Markus V. Paananen, Willem van Mechelen, and Simo Taimela (2014). The influence of selective participation in a physical activity intervention on the generalizability of findings. Journal of Occupational and Environmental Medicine, 56(3):291–297. Online 13 January 2014
• Jarmo Rantonen, Aki Vehtari, Jaro Karppinen, Satu Luoto, Eira Viikari-Juntura, Markku Hupli, Antti Malmivaara and Simo Taimela (2014). Face-to-face information in addition to a booklet versus a booklet alone for treating mild back pain, a randomized controlled trial. Scandinavian journal of Work Environment & Health, 40(2):156-166. Online 2 November, 2013.
• Mari Myllymäki, Aila Särkkä and Aki Vehtari (2014). Hierarchical second-order analysis of replicated spatial point patterns with non-spatial covariates. Spatial Statistics, 8:104-121. Online 13 August, 2013. Preprint.
• Andrew Gelman, Jessica Hwang and Aki Vehtari (2014). Understanding predictive information criteria for Bayesian models. Statistics and Computing, 24(6):997-1016. Online 20 August, 2013. Preprint.

2013

• Andrew Gelman, John B. Carlin, Hal S. Stern, David B. Dunson, Aki Vehtari and Donald B. Rubin (2013). Bayesian Data Analysis, Third Edition. Chapman and Hall/CRC. Publisher's webpage for the book. Home page for the book. Errata for 3rd edition.
• Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen, Pasi Jylänki, Ville Tolvanen and Aki Vehtari (2013). GPstuff: Bayesian modeling with Gaussian processes. Journal of Machine Learning Research, 14(Apr):1175-1179. Online. Software homepage.
• Jaakko Riihimäki, Pasi Jylänki and Aki Vehtari (2013). Nested expectation propagation for Gaussian process classification with a multinomial probit likelihood. Journal of Machine Learning Research, 14(Jan):75-109. Online. Code available in GPstuff toolbox.

2012

• Aki Vehtari and Janne Ojanen (2012). A survey of Bayesian predictive methods for model assessment, selection and comparison. Statistics Surveys, 6:142-228. Online. Errata was published in Statistics Surveys, 8 (2014), 1-1.
• Tomi Peltola, Pekka Marttinen and Aki Vehtari (2012). Finite adaptation and multistep moves in the Metropolis-Hastings algorithm for variable selection in genome-wide association analysis. In PLoS One, 7(11):e49445. Online.
• Karita Reijonsaari, Aki Vehtari, Olli-Pekka Kahilakoski, Willem van Mechelen, Timo Aro and Simo Taimela (2012). The effectiveness of physical activity monitoring and distance counseling in an occupational setting - Results from a randomized controlled trial (CoAct). BMC Public Health, 12:344 (11 May 2012). Online.
• Heikki Joensuu, Mikael Eriksson, Kirsten Sundby Hall, Jörg T. Hartmann, Daniel Pink, Jochen Schütte, Giuliano Ramadori, Peter Hohenberger, Justus Duyster, Salah-Eddin Al-Batran, Marcus Schlemmer, Sebastian Bauer, Eva Wardelmann, Maarit Sarlomo-Rikala, Bengt Nilsson, Harri Sihto, Odd R. Monge, Petri Bono, Raija Kallio, Aki Vehtari, Mika Leinonen, Thor Alvegård and Peter Reichardt (2012). One vs three years of adjuvant imatinib for operable gastrointestinal stromal tumor: A randomized trial. The Journal of American Medical Association, 307(12):1265-1272. Online. Featured article. Top 10 Journal Watch Oncology and Hematology story.
• Heikki Joensuu, Aki Vehtari, Jaakko Riihimäki, Toshirou Nishida, Sonja E Steigen, Peter Brabec, Lukas Plank, Bengt Nilsson, Claudia Cirilli, Chiara Braconi, Andrea Bordoni, Magnus K Magnusson, Zdenek Linke, Jozef Sufliarsky, Federico Massimo, Jon G Jonasson, Angelo Paolo Dei Tos and Piotr Rutkowski (2012). Risk of gastrointestinal stromal tumour recurrence after surgery: an analysis of pooled population-based cohorts. In The Lancet Oncology, 13(3):265-274. Published Online: 07 December 2011. Statistical appendix. Commented in editorial.
• Simo Särkkä, Arno Solin, Aapo Nummenmaa, Aki Vehtari, Toni Auranen, Simo Vanni and Fa-Hsuan Lin (2012). Dynamic Retrospective Filtering of Physiological Noise in BOLD fMRI: DRIFTER. NeuroImage, 60(2):1517-1527. Available online, Preprint.
• Tomi Peltola, Pekka Marttinen, Antti Jula, Veikko Salomaa, Markus Perola and Aki Vehtari (2012). Bayesian variable selection in searching for additive and dominant effects in genome-wide data. PLoS ONE, 7(1):e29115. Online. Code.
• Jarmo Rantonen, Satu Luoto, Aki Vehtari, Markku Hupli, Jaro Karppinen, Antti Malmivaara and Simo Taimela (2012). The effectiveness of two active interventions compared to self-care advice in employees with non-acute low back symptoms. A randomised, controlled trial with a 4-year follow-up in the occupational health setting. Occupational and Environmental Medicine, 69(1):12-20. Available online. Editor's choice.

2011

• Pasi Jylänki, Jarno Vanhatalo and Aki Vehtari (2011). Robust Gaussian process regression with a Student-t likelihood. Journal of Machine Learning Research, 12(Nov):3227-3257. Online. Code available in GPstuff toolbox.

2010

• Jarno Vanhatalo, Pia Mäkelä and Aki Vehtari (2010). Alkoholikuolleisuuden alueelliset erot Suomessa 2000-luvun alussa. Yhteiskuntapolitiikka, 75(3):265-273. Online in Finnish. English translation: Regional differences in alcohol mortality in Finland in the early 2000s. Online maps in Finnish.
• Jaakko Riihimäki and Aki Vehtari (2010). Gaussian processes with monotonicity information. Journal of Machine Learning Research: Workshop and Conference Proceedings, 9:645-652, (AISTATS 2010 Proceedings). Online. Code available in GPstuff toolbox.
• Jarno Vanhatalo, Ville Pietiläinen and Aki Vehtari (2010). Approximate inference for disease mapping with sparse Gaussian processes. Statistics in Medicine, 29(15):1580-1607. Online.
• Jarno Vanhatalo and Aki Vehtari (2010). Speeding up the binary Gaussian process classification. In P. Grünwald and P. Spirtes, editors, Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010), pp. 623-632, AUAI Press. Online.
• Elina Parviainen and Aki Vehtari (2010). Explaining classification by finding response-related subgroups in data. In Ma, J. et al, eds., Proceedings of the 11th ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing, SNPD2010, pp. 69-75, IEEE Computer Society.

2009

• Karita Reijonsaari, Aki Vehtari, Willem Van Mechelen, Timo Aro and Simo Taimela (2009). The effectiveness of physical activity monitoring and distance counselling in an occupational health setting - a research protocol for a randomised controlled trial (CoAct). BMC Public Health, 9:494. Available online.
• Jaakko Riihimäki, Reijo Sund and Aki Vehtari (2009). Analysing the length of care episode after hip fracture: a nonparametric and a parametric Bayesian approach. Health Care Management Science, 10.1007/s10729-009-9121-z. Available online 13 November 2009.
• Jarno Vanhatalo, Pasi Jylänki and Aki Vehtari (2009). Gaussian process regression with Student-t likelihood. In Y. Bengio et al, editors, Advances in Neural Information Processing Systems 22, pp. 1910-1918, NIPS Foundation. Available online.
• Petri Korhonen, Terhi Husa, Teijo Konttila, Ilkka Tierala, Markku Mäkijärvi, Heikki Väänänen, Janne Ojanen, Aki Vehtari and Lauri Toivonen (2009). Fragmented QRS in prediction of cardiac deaths and heart failure hospitalizations after myocardial infarction. Annals of Noninvasive Electrocardiology, 15(2):130--137.
• Reijo Sund, Jaakko Riihimäki, Matti Mäkelä, Aki Vehtari, Peter Lüthje, Tiina Huusko and Unto Häkkinen (2009). Modeling the length of care episode after hip fracture: does the type of fracture matter? Scandinavian Journal of Surgery, 98(3):169-174.
• Elina Parviainen and Aki Vehtari (2009). Features and metric from a classifier improve visualizations with dimension reduction. In Alippi et al, eds. Artificial Neural Networks - ICANN 2009, part II, pp. 225--234, Springer.

2008

• Toni Auranen, Aapo Nummenmaa, Simo Vanni, Aki Vehtari, Matti S. Hämäläinen, Jouko Lampinen and Iiro P. Jääskeläinen (2009). Automatic fMRI-guided MEG multidipole localization for visual responses. Human Brain Mapping, 30(4):1087-1099. Available online 8 May 2008.
• Jarno Vanhatalo and Aki Vehtari (2008). Modelling local and global phenomena with sparse Gaussian processes. In David McAllester and Petri Myllymäki, editors, Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence (UAI 2008), pp. 571-578, AUAI Press. Available online.
• Taru Tukiainen, Tuulia Tynkkynen, Ville-Petteri Mäkinen, Pasi Jylänki, Antti Kangas, Johanna Hokkanen, Aki Vehtari, Olli Gröhn, Merja Hallikainen, Hilkka Soininen, Miia Kivipelto, Per-Henrik Groop, Kimmo Kaski, Reino Laatikainen, Pasi Soininen, Tuula Pirttilä and Mika Ala-Korpela (2008). A multi-metabolite analysis of serum by 1H NMR spectroscopy: early systemic signs of Alzheimer's disease. Biochemical and Biophysical Research Communications, 375(3):356-61.

2007

• Aki Vehtari, Ville-Petteri Mäkinen, Pasi Soininen, Petri Ingman, Sanna M. Mäkelä, Markku J. Savolainen, Minna L. Hannuksela, Kimmo Kaski and Mika Ala-Korpela (2007). A novel Bayesian approach to quantify clinical variables and to determine their spectroscopic counterparts in 1H NMR metabonomic data. BMC Bioinformatics, 8(Suppl 2):S8. Available online.
• Jarno Vanhatalo and Aki Vehtari (2007). Sparse log Gaussian processes via MCMC for spatial epidemiology. In Journal of Machine Learning Research: Workshop and Conference Proceedings, 1:73-89. Gaussian Processes in Practice special issue. Abstract, PDF.
• Marko Tapani Sysi-Aho, Aki Vehtari, Vidya Velagapudi, Jukka Westerbacka, Laxman Yetukuri, Robert Bergholm, Marja-Riitta Taskinen, Hannele Yki-Järvinen and Matej Oresic (2007). Exploring the lipoprotein composition using Bayesian regression on serum lipidomic profiles. Bioinformatics, 23(13):i519-i528, 2007. Available online.
• Aapo Nummenmaa, Toni Auranen, Matti S Hämäläinen, Iiro P Jääskeläinen, Mikko Sams, Aki Vehtari and Jouko Lampinen (2007). Automatic relevance-determination based hierarchical Bayesian MEG inversion in practice. NeuroImage, 37(3):876-889. Available online.
• Toni Auranen, Aapo Nummenmaa, Matti S. Hämäläinen, Iiro P. Jääskeläinen, Jouko Lampinen, Aki Vehtari and Mikko Sams (2007). Bayesian inverse analysis of neuromagnetic data using cortically constrained multiple dipoles. Human Brain Mapping, 28(10):979-994. Available online 16 March 2007.
• Aapo Nummenmaa, Toni Auranen, Matti S. Hämäläinen, Iiro P. Jääskeläinen, Jouko Lampinen, Mikko Sams and Aki Vehtari (2007). Hierarchical Bayesian estimates of distributed MEG sources: theoretical aspects and comparison of variational and MCMC methods. NeuroImage, 35(2):669-685. Available online 12 February 2007.
• Simo Särkkä, Aki Vehtari and Jouko Lampinen (2007). CATS benchmark time series prediction by Kalman smoother with cross-validated noise density. Neurocomputing, 70(13-15):2331-2341. Available online 22 February 2007, preprint PDF.
• Simo Särkkä, Aki Vehtari and Jouko Lampinen (2007). Rao-Blackwellized Particle Filter for Multiple Target Tracking. Information Fusion, 8(1):2-15. Available online, preprint PDF.
• Simo Särkkä, Aki Vehtari and Jouko Lampinen (2007). Prediction of ESTSP Competition Time Series by Unscented Kalman Filter and RTS Smoother. In Amaury Lendasse, editor, Proceedings of European Symposium on Time Series Prediction (ESTSP'07), pp.1-10. PDF.

2005

• Toni Auranen, Aapo Nummenmaa, Matti S. Hämäläinen, Iiro P. Jääskeläinen, Jouko Lampinen, Aki Vehtari and Mikko Sams (2005). Bayesian analysis of the neuromagnetic inverse problem with L^p-norm priors. NeuroImage, 26(3):870-884. Revised personal version PDF.
• Ilkka Kalliomäki, Aki Vehtari and Jouko Lampinen (2005). Shape analysis of concrete aggregates for statistical quality modeling. Machine Vision and Applications, 16(3):197-201. PDF.

2004

• Simo Särkkä, Aki Vehtari and Jouko Lampinen (2004). Time series prediction by Kalman smoother with cross-validated noise density. In IJCNN'2004: Proceedings of the 2004 International Joint Conference on Neural Networks, Budabest, July 2004. The Winner of Time Series Prediction Competition - The CATS Benchmark. PDF.
• Simo Särkkä, Aki Vehtari and Jouko Lampinen (2004). Rao-Blackwellized Monte Carlo data association for multiple target tracking. In Per Svensson and Johan Schubert, editors, Proceedings of the Seventh International Conference on Information Fusion, volume I, pp. 583-590. PDF.

2003

• Aki Vehtari and Jouko Lampinen (2003). Expected utility estimation via cross-validation. In J. M. Bernardo, et al., editors, Bayesian Statistics 7, pp. 701-710. Oxford University Press. PDF.

2002

• Aki Vehtari and Jouko Lampinen (2002). Bayesian model assessment and comparison using cross-validation predictive densities. Neural Computation, 14(10):2439-2468. Online. Preprint.

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