Associate professor in computational science (focus on probabilistic modeling) at Aalto University
Visiting professor at Technical University of Denmark (DTU)
- School of Science
Department of Computer Science
- Office: Konemiehentie 2, 3rd floor, A314
- Phone: +358 40 5333 747
- Email: Aki.Vehtari(at)aalto.fi
- Twitter: @avehtari
My research interests are Bayesian probability theory and
methodology, especially inference methods such as Laplace, EP,
VB, MC, model assessment and selection, non-parametric models
such as Gaussian processes, dynamic models, and hierarchical
Applications include brain signal analysis (MEG, fMRI,
InI-fMRI ), cancer survival analysis, public and
occupational health care data analysis, spatio-temporal
epidemiology and large predator population structure and size
BDA3: 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.
My blog postings
GPstuff - Gaussian process models for Bayesian analysis (software)
Full list of my publications
- Juho Piironen and Aki Vehtari (2015). Comparison of Bayesian predictive methods for model selection. Preprint.
- Aki Vehtari, Tommi Mononen, Ville Tolvanen and Ole Winther (2014). Bayesian leave-one-out cross-validation approximations for Gaussian latent variable models. Preprint.
- 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.
- Aki Vehtari and Andrew Gelman (2014). WAIC and cross-validation in Stan. PDF
- Ville Tolvanen, Pasi Jylänki and Aki Vehtari
(2014). Expectation propagation for nonstationary heteroscedastic
Gaussian process regression. In Proceedings of IEEE
International Workshop on Machine Learning for Signal Processing,
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 Proceedings of Eleventh Bayesian UAI Bayesian Modeling Applications Workshop, accepted for publication. Preprint
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
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.
Last modified: 2015-05-01