Application of Bayesian neural network in electrical impedance tomography. - Jouko Lampinen; Aki Vehtari; Kimmo Leinonen (IJCNN, 1999)

Bayesian neural networks with correlating residuals. - Aki Vehtari; Jouko Lampinen (IJCNN, 1999)

Bayesian techniques for neural networks - Review and case studies. - Jouko Lampinen; Aki Vehtari (EUSIPCO, 2000)

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

Bayesian MLP neural networks for image analysis. - Aki Vehtari; Jouko Lampinen (Pattern Recognit. Lett., 2000)

Bayesian approach for neural networks–review and case studies. - Jouko Lampinen; Aki Vehtari (Neural Networks, 2001)

Bayesian Model Assessment and Comparison Using Cross-Validation Predictive Densities. - Aki Vehtari; Jouko Lampinen (Neural Comput., 2002)

Time series prediction by Kalman smoother with cross-validated noise density. - Simo Särkkä; Aki Vehtari; Jouko Lampinen (IJCNN, 2004)

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

Bayesian analysis of the neuromagnetic inverse problem with ℓp-norm priors. - Toni Auranen; Aapo Nummenmaa; Matti S. Hämäläinen; Iiro P. Jääskeläinen; Jouko Lampinen; Aki Vehtari; Mikko Sams (NeuroImage, 2005)

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

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

CATS benchmark time series prediction by Kalman smoother with cross-validated noise density. - Simo Särkkä; Aki Vehtari; Jouko Lampinen (Neurocomputing, 2007)

Rao-Blackwellized particle filter for multiple target tracking. - Simo Särkkä; Aki Vehtari; Jouko Lampinen (Inf. Fusion, 2007)

Sparse Log Gaussian Processes via MCMC for Spatial Epidemiology. - Jarno Vanhatalo; Aki Vehtari (Gaussian Processes in Practice, 2007)

Hierarchical Bayesian estimates of distributed MEG sources: Theoretical aspects and comparison of variational and MCMC methods. - Aapo Nummenmaa; Toni Auranen; Matti S. Hämäläinen; Iiro P. Jääskeläinen; Jouko Lampinen; Mikko Sams; Aki Vehtari (NeuroImage, 2007)

Automatic relevance determination based hierarchical Bayesian MEG inversion in practice. - Aapo Nummenmaa; Toni Auranen; Matti S. Hämäläinen; Iiro P. Jääskeläinen; Mikko Sams; Aki Vehtari; Jouko Lampinen (NeuroImage, 2007)

Modelling local and global phenomena with sparse Gaussian processes. - Jarno Vanhatalo; Aki Vehtari (UAI, 2008)

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

Gaussian process regression with Student-t likelihood. - Jarno Vanhatalo; Pasi Jylänki; Aki Vehtari (NIPS, 2009)

Explaining Classification by Finding Response-Related Subgroups in Data. - Elina Parviainen; Aki Vehtari (SNPD, 2010)

Speeding up the binary Gaussian process classification. - Jarno Vanhatalo; Aki Vehtari (UAI, 2010)

Gaussian processes with monotonicity information. - Jaakko Riihimäki; Aki Vehtari (AISTATS, 2010)

Robust Gaussian Process Regression with a Student-t Likelihood. - Pasi Jylänki; Jarno Vanhatalo; Aki Vehtari (J. Mach. Learn. Res., 2011)

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

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

Nested expectation propagation for Gaussian process classification. - Jaakko Riihimäki; Pasi Jylänki; Aki Vehtari (J. Mach. Learn. Res., 2013)

GPstuff: Bayesian modeling with Gaussian processes. - Jarno Vanhatalo; Jaakko Riihimäki; Jouni Hartikainen; Pasi Jylänki; Ville Tolvanen; Aki Vehtari (J. Mach. Learn. Res., 2013)

Expectation Propagation for Likelihoods Depending on an Inner Product of Two Multivariate Random Variables. - Tomi Peltola; Pasi Jylänki; Aki Vehtari (AISTATS, 2014)

Expectation propagation for nonstationary heteroscedastic Gaussian process regression. - Ville Tolvanen; Pasi Jylänki; Aki Vehtari (MLSP, 2014)

Hierarchical Bayesian Survival Analysis and Projective Covariate Selection in Cardiovascular Event Risk Prediction. - Tomi Peltola; Aki S. Havulinna; Veikko Salomaa; Aki Vehtari (BMA@UAI, 2014)

Expectation propagation for neural networks with sparsity-promoting priors. - Pasi Jylänki; Aapo Nummenmaa; Aki Vehtari (J. Mach. Learn. Res., 2014)

Understanding predictive information criteria for Bayesian models. - Andrew Gelman; Jessica Hwang; Aki Vehtari (Stat. Comput., 2014)

Chained Gaussian Processes. - Alan D. Saul; James Hensman; Aki Vehtari; Neil D. Lawrence (AISTATS, 2016)

Projection predictive model selection for Gaussian processes. - Juho Piironen; Aki Vehtari (MLSP, 2016)

Automatic detection of acute kidney injury episodes from primary care data. - Santosh Tirunagari; Simon C. Bull; Aki Vehtari; Christopher Farmer; Simon de Lusignan; Norman Poh (SSCI, 2016)

Bayesian Leave-One-Out Cross-Validation Approximations for Gaussian Latent Variable Models. - Aki Vehtari; Tommi Mononen; Ville Tolvanen; Tuomas Sivula; Ole Winther (J. Mach. Learn. Res., 2016)

On the Hyperprior Choice for the Global Shrinkage Parameter in the Horseshoe Prior. - Juho Piironen; Aki Vehtari (AISTATS, 2017)

ELFI: Engine for Likelihood Free Inference. - Jarno Lintusaari; Henri Vuollekoski; Antti Kangasrääsiö; Kusti Skytén; Marko Järvenpää; Michael U. Gutmann; Aki Vehtari; Jukka Corander; Samuel Kaski (CoRR, 2017)

Bayesian Inference for Spatio-temporal Spike-and-Slab Priors. - Michael Riis Andersen; Aki Vehtari; Ole Winther; Lars Kai Hansen (J. Mach. Learn. Res., 2017)

Distributed neural signatures of natural audiovisual speech and music in the human auditory cortex. - Juha Salmi; Olli-Pekka Koistinen; Enrico Glerean; Pasi Jylänki; Aki Vehtari; Iiro P. Jääskeläinen; Sasu Mäkelä; Lauri Nummenmaa; Katarina Nummi-Kuisma; Ilari Nummi; Mikko Sams (NeuroImage, 2017)

Comparison of Bayesian predictive methods for model selection. - Juho Piironen; Aki Vehtari (Stat. Comput., 2017)

Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. - Aki Vehtari; Andrew Gelman; Jonah Gabry (Stat. Comput., 2017)

Erratum to: Practical Bayesian model evaluation using leave-one-out cross-validation and WAIC. - Aki Vehtari; Andrew Gelman; Jonah Gabry (Stat. Comput., 2017)

Iterative Supervised Principal Components. - Juho Piironen; Aki Vehtari (AISTATS, 2018)

Yes, but Did It Work?: Evaluating Variational Inference. - Yuling Yao; Aki Vehtari; Daniel Simpson; Andrew Gelman (ICML, 2018)

User Modelling for Avoiding Overfitting in Interactive Knowledge Elicitation for Prediction. - Pedram Daee; Tomi Peltola; Aki Vehtari; Samuel Kaski (IUI, 2018)

Correcting boundary over-Exploration Deficiencies in Bayesian Optimization with Virtual derivative Sign observations. - Eero Siivola; Aki Vehtari; Jarno Vanhatalo; Javier González 002; Michael Riis Andersen (MLSP, 2018)

Projective Inference in High-dimensional Problems: Prediction and Feature Selection. - Juho Piironen; Markus Paasiniemi; Aki Vehtari (CoRR, 2018)

ELFI: Engine for Likelihood-Free Inference. - Jarno Lintusaari; Henri Vuollekoski; Antti Kangasrääsiö; Kusti Skytén; Marko Järvenpää; Pekka Marttinen; Michael U. Gutmann; Aki Vehtari; Jukka Corander; Samuel Kaski (J. Mach. Learn. Res., 2018)

Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution. - Topi Paananen; Juho Piironen; Michael Riis Andersen; Aki Vehtari (AISTATS, 2019)

Bayesian leave-one-out cross-validation for large data. - Måns Magnusson; Michael Riis Andersen; Johan Jonasson; Aki Vehtari (ICML, 2019)

Active Learning for Decision-Making from Imbalanced Observational Data. - Iiris Sundin; Peter Schulam; Eero Siivola; Aki Vehtari; Suchi Saria; Samuel Kaski (ICML, 2019)

Parallel Gaussian process surrogate method to accelerate likelihood-free inference. - Marko Järvenpää; Michael U. Gutmann; Aki Vehtari; Pekka Marttinen (CoRR, 2019)

Batch simulations and uncertainty quantification in Gaussian process surrogate-based approximate Bayesian computation. - Marko Järvenpää; Aki Vehtari; Pekka Marttinen (CoRR, 2019)

Making Bayesian Predictive Models Interpretable: A Decision Theoretic Approach. - Homayun Afrabandpey; Tomi Peltola; Juho Piironen; Aki Vehtari; Samuel Kaski (CoRR, 2019)

An interpretable probabilistic machine learning method for heterogeneous longitudinal studies. - Juho Timonen; Henrik Mannerström; Aki Vehtari; Harri Lähdesmäki (CoRR, 2019)

Leave-One-Out Cross-Validation for Bayesian Model Comparison in Large Data. - Måns Magnusson; Aki Vehtari; Johan Jonasson; Michael Riis Andersen (AISTATS, 2020)

Scalable Gaussian Process for Extreme Classification. - Akash Kumar Dhaka; Michael Riis Andersen; Pablo Garcia Moreno; Aki Vehtari (MLSP, 2020)

Robust, Accurate Stochastic Optimization for Variational Inference. - Akash Kumar Dhaka; Alejandro Catalina; Michael Riis Andersen; Måns Magnusson; Jonathan H. Huggins; Aki Vehtari (NeurIPS, 2020)

Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond. - Charles C. Margossian; Aki Vehtari; Daniel Simpson; Raj Agrawal (NeurIPS, 2020)

Batch simulations and uncertainty quantification in Gaussian process surrogate approximate Bayesian computation. - Marko Järvenpää; Aki Vehtari; Pekka Marttinen (UAI, 2020)

A Fast Linear Regression via SVD and Marginalization. - Philip Greengard; Andrew Gelman; Aki Vehtari (CoRR, 2020)

Good practices for Bayesian Optimization of high dimensional structured spaces. - Eero Siivola; Javier González 002; Andrei Paleyes; Aki Vehtari (CoRR, 2020)

Expectation Propagation as a Way of Life: A Framework for Bayesian Inference on Partitioned Data. - Aki Vehtari; Andrew Gelman; Tuomas Sivula; Pasi Jylänki; Dustin Tran; Swupnil Sahai; Paul Blomstedt; John P. Cunningham; David Schiminovich; Christian P. Robert (J. Mach. Learn. Res., 2020)

A decision-theoretic approach for model interpretability in Bayesian framework. - Homayun Afrabandpey; Tomi Peltola; Juho Piironen; Aki Vehtari; Samuel Kaski (Mach. Learn., 2020)

Interindividual variability and lateralization of μ-opioid receptors in the human brain. - Tatu Kantonen; Tomi Karjalainen; Janne Isojärvi; Pirjo Nuutila; Jouni Tuisku; Juha O. Rinne; Jarmo Hietala; Valtteri Kaasinen; Kari Kalliokoski; Harry Scheinin; Jussi Hirvonen; Aki Vehtari; et al. (NeuroImage, 2020)

Preferential Batch Bayesian Optimization. - Eero Siivola; Akash Kumar Dhaka; Michael Riis Andersen; Javier González 002; Pablo Garcia Moreno; Aki Vehtari (MLSP, 2021)

Challenges and Opportunities in High Dimensional Variational Inference. - Akash Kumar Dhaka; Alejandro Catalina; Manushi Welandawe; Michael Riis Andersen; Jonathan H. Huggins; Aki Vehtari (NeurIPS, 2021)

Uncertainty-aware sensitivity analysis using Rényi divergences. - Topi Paananen; Michael Riis Andersen; Aki Vehtari (UAI, 2021)

lgpr: an interpretable non-parametric method for inferring covariate effects from longitudinal data. - Juho Timonen; Henrik Mannerström; Aki Vehtari; Harri Lähdesmäki (Bioinform., 2021)

Bayesian hierarchical stacking. - Yuling Yao; Gregor Pirs; Aki Vehtari; Andrew Gelman (CoRR, 2021)

Efficient leave-one-out cross-validation for Bayesian non-factorized normal and Student-t models. - Paul-Christian Bürkner; Jonah Gabry; Aki Vehtari (Comput. Stat., 2021)

Implicitly adaptive importance sampling. - Topi Paananen; Juho Piironen; Paul-Christian Bürkner; Aki Vehtari (Stat. Comput., 2021)

Projection Predictive Inference for Generalized Linear and Additive Multilevel Models. - Alejandro Catalina; Paul-Christian Bürkner; Aki Vehtari (AISTATS, 2022)

Feature Collapsing for Gaussian Process Variable Ranking. - Isaac Sebenius; Topi Paananen; Aki Vehtari (AISTATS, 2022)

Robust, Automated, and Accurate Black-box Variational Inference. - Manushi Welandawe; Michael Riis Andersen; Aki Vehtari; Jonathan H. Huggins (CoRR, 2022)

A fast regression via SVD and marginalization. - Philip Greengard; Andrew Gelman; Aki Vehtari (Comput. Stat., 2022)

Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors. - Yuling Yao; Aki Vehtari; Andrew Gelman (J. Mach. Learn. Res., 2022)

Pathfinder: Parallel quasi-Newton variational inference. - Lu Zhang; Bob Carpenter; Andrew Gelman; Aki Vehtari (J. Mach. Learn. Res., 2022)

Atlas of type 2 dopamine receptors in the human brain: Age and sex dependent variability in a large PET cohort. - Tuulia Malén; Tomi Karjalainen; Janne Isojärvi; Aki Vehtari; Paul-Christian Bürkner; Vesa Putkinen; Valtteri Kaasinen; Jarmo Hietala; Pirjo Nuutila; Juha O. Rinne; Lauri Nummenmaa (NeuroImage, 2022)

Graphical test for discrete uniformity and its applications in goodness-of-fit evaluation and multiple sample comparison. - Teemu Säilynoja; Paul-Christian Bürkner; Aki Vehtari (Stat. Comput., 2022)

Practical Hilbert space approximate Bayesian Gaussian processes for probabilistic programming. - Gabriel Riutort-Mayol; Paul-Christian Bürkner; Michael Riis Andersen; Arno Solin; Aki Vehtari (Stat. Comput., 2023)

Using reference models in variable selection. - Federico Pavone; Juho Piironen; Paul-Christian Bürkner; Aki Vehtari (Comput. Stat., 2023)

Active Learning of Molecular Data for Task-Specific Objectives. - Kunal Ghosh; Milica Todorovic; Aki Vehtari; Patrick Rinke (CoRR, 2024)

Amortized Bayesian Workflow (Extended Abstract). - Marvin Schmitt; Chengkun Li; Aki Vehtari; Luigi Acerbi; Paul-Christian Bürkner; Stefan T. Radev (CoRR, 2024)

Predicting habitat suitability for Asian elephants in non-analog ecosystems with Bayesian models. - Ryoko Noda; Michael Mechenich; Juha Saarinen; Aki Vehtari; Indre Zliobaite (Ecol. Informatics, 2024)

Pareto Smoothed Importance Sampling. - Aki Vehtari; Daniel Simpson; Andrew Gelman; Yuling Yao; Jonah Gabry (J. Mach. Learn. Res., 2024)

A Framework for Improving the Reliability of Black-box Variational Inference. - Manushi Welandawe; Michael Riis Andersen; Aki Vehtari; Jonathan H. Huggins (J. Mach. Learn. Res., 2024)

Detecting and diagnosing prior and likelihood sensitivity with power-scaling. - Noa Kallioinen; Topi Paananen; Paul-Christian Bürkner; Aki Vehtari (Stat. Comput., 2024)

Bayesian cross-validation by parallel Markov chain Monte Carlo. - Alex Cooper; Aki Vehtari; Catherine Forbes; Daniel Simpson; Lauren Kennedy (Stat. Comput., 2024)

Efficient estimation and correction of selection-induced bias with order statistics. - Yann McLatchie; Aki Vehtari (Stat. Comput., 2024)

posteriordb: Testing, Benchmarking and Developing Bayesian Inference Algorithms. - Måns Magnusson; Jakob Torgander; Paul-Christian Bürkner; Lu Zhang; Bob Carpenter; Aki Vehtari (AISTATS, 2025)

Projection predictive variable selection for discrete response families with finite support. - Frank Weber; Änne Glass; Aki Vehtari (Comput. Stat., 2025)