| Sparsity information and regularization in the horseshoe and other shrinkage priors J Piironen, A Vehtari | 515 | 2017 |
| Comparison of Bayesian predictive methods for model selection J Piironen, A Vehtari Statistics and Computing 27, 711-735, 2017 | 398 | 2017 |
| On the hyperprior choice for the global shrinkage parameter in the horseshoe prior J Piironen, A Vehtari Artificial intelligence and statistics, 905-913, 2017 | 141 | 2017 |
| Projective inference in high-dimensional problems: Prediction and feature selection J Piironen, M Paasiniemi, A Vehtari | 133 | 2020 |
| Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution T Paananen, J Piironen, MR Andersen, A Vehtari The 22nd international conference on artificial intelligence and statistics …, 2019 | 61 | 2019 |
| Projection predictive model selection for Gaussian processes J Piironen, A Vehtari 2016 IEEE 26th international workshop on machine learning for signal …, 2016 | 59 | 2016 |
| Implicitly adaptive importance sampling T Paananen, J Piironen, PC Bürkner, A Vehtari Statistics and Computing 31 (2), 16, 2021 | 51 | 2021 |
| Using reference models in variable selection F Pavone, J Piironen, PC Bürkner, A Vehtari Computational Statistics 38 (1), 349-371, 2023 | 30 | 2023 |
| Projection predictive variable selection using Stan+ R J Piironen, A Vehtari arXiv preprint arXiv:1508.02502, 2015 | 30 | 2015 |
| Iterative supervised principal components J Piironen, A Vehtari International Conference on Artificial Intelligence and Statistics, 106-114, 2018 | 21 | 2018 |
| Projpred: projection predictive feature selection J Piironen, M Paasiniemi, A Catalina, A Vehtari R package version 2 (0), 2023 | 19 | 2023 |
| A decision-theoretic approach for model interpretability in Bayesian framework H Afrabandpey, T Peltola, J Piironen, A Vehtari, S Kaski Machine learning 109 (9), 1855-1876, 2020 | 18 | 2020 |
| Bayesian estimation of Gaussian graphical models with predictive covariance selection DR Williams, J Piironen, A Vehtari, P Rast arXiv preprint arXiv:1801.05725, 2018 | 15 | 2018 |
| Predicting spatio‐temporal distributions of migratory populations using Gaussian process modelling A Piironen, J Piironen, T Laaksonen Journal of Applied Ecology 59 (4), 1146-1156, 2022 | 7 | 2022 |
| Alarm prediction in LTE networks S Holmbacka, J Niemelä, H Karikallio, K Sunila, I Raiskinen, E Siivola, ... 2018 25th International Conference on Telecommunications (ICT), 341-345, 2018 | 7 | 2018 |
| Bayesian estimation of Gaussian graphical models with projection predictive selection DR Williams, J Piironen, A Vehtari, P Rast arXiv preprint arXiv:1801.05725, 2018 | 7 | 2018 |
| projpred: Projection Predictive Feature Selection.(2019) J Piironen, M Paasiniemi, A Vehtari, J Gabry, PC Bürkner | 6 | |
| Contributed comment on article by van der Pas, Szabó, and van der Vaart J Piironen, M Betancourt, D Simpson, A Vehtari Bayesian Analysis 12 (4), 1264-1266, 2017 | 5 | 2017 |
| Automatic monotonicity detection for gaussian processes E Siivola, J Piironen, A Vehtari arXiv preprint arXiv:1610.05440, 2016 | 5 | 2016 |
| Making Bayesian predictive models interpretable: A decision theoretic approach H Afrabandpey, T Peltola, J Piironen, A Vehtari, S Kaski arXiv: 1910.09358, 2019 | 4 | 2019 |
Aki VehtariProfessor, Aalto UniversityVerified email at aalto.fi
