| GPstuff: Bayesian modeling with Gaussian processes J Vanhatalo, J Riihimäki, J Hartikainen, P Jylänki, V Tolvanen, A Vehtari Journal of Machine Learning Research 14 (Apr), 1175-1179, 2013 | 319* | 2013 |
| A comprehensive evaluation of predictive performance of 33 species distribution models at species and community levels A Norberg, N Abrego, FG Blanchet, FR Adler, BJ Anderson, J Anttila, ... Ecological monographs 89 (3), e01370, 2019 | 171 | 2019 |
| Robust Gaussian Process Regression with a Student-t Likelihood. P Jylänki, J Vanhatalo, A Vehtari Journal of Machine Learning Research 12 (11), 2011 | 162 | 2011 |
| Gaussian process regression with Student-t likelihood J Vanhatalo, P Jylänki, A Vehtari Advances in neural information processing systems 22, 2009 | 87 | 2009 |
| Approximate inference for disease mapping with sparse Gaussian processes J Vanhatalo, V Pietiläinen, A Vehtari Statistics in medicine 29 (15), 1580-1607, 2010 | 75 | 2010 |
| Non-stationary Gaussian models with physical barriers H Bakka, J Vanhatalo, JB Illian, D Simpson, H Rue Spatial statistics 29, 268-288, 2019 | 69* | 2019 |
| Preparing for the unprecedented—Towards quantitative oil risk assessment in the Arctic marine areas M Nevalainen, I Helle, J Vanhatalo Marine Pollution Bulletin 114 (1), 90-101, 2017 | 64 | 2017 |
| Sparse log Gaussian processes via MCMC for spatial epidemiology J Vanhatalo, A Vehtari Gaussian processes in practice, 73-89, 2007 | 52 | 2007 |
| Spatiotemporal modelling of crown‐of‐thorns starfish outbreaks on the Great Barrier Reef to inform control strategies J Vanhatalo, GR Hosack, H Sweatman Journal of Applied Ecology 54 (1), 188-197, 2017 | 51 | 2017 |
| Modelling local and global phenomena with sparse Gaussian processes J Vanhatalo, A Vehtari Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, 2008 | 50* | 2008 |
| Predicting ice-induced load amplitudes on ship bow conditional on ice thickness and ship speed in the Baltic Sea M Kotilainen, J Vanhatalo, M Suominen, P Kujala Cold Regions Science and Technology 135, 116-126, 2017 | 46 | 2017 |
| Modeling the spatial distribution of larval fish abundance provides essential information for management M Kallasvuo, J Vanhatalo, L Veneranta Canadian Journal of Fisheries and Aquatic Sciences 74 (5), 636-649, 2017 | 43 | 2017 |
| The value of reducing eutrophication in European marine areas—A Bayesian meta-analysis H Ahtiainen, J Vanhatalo Ecological Economics 83, 1-10, 2012 | 39 | 2012 |
| Integrating experimental and distribution data to predict future species patterns J Kotta, J Vanhatalo, H Jänes, H Orav-Kotta, L Rugiu, V Jormalainen, ... Scientific reports 9 (1), 1-14, 2019 | 37 | 2019 |
| Experiences in Bayesian inference in Baltic salmon management S Kuikka, J Vanhatalo, H Pulkkinen, S Mäntyniemi, J Corander Statistical Science 29 (1), 42-49, 2014 | 36 | 2014 |
| Impacts of oil spills on Arctic marine ecosystems: A quantitative and probabilistic risk assessment perspective I Helle, J Mäkinen, M Nevalainen, M Afenyo, J Vanhatalo Environmental science & technology 54 (4), 2112-2121, 2020 | 34 | 2020 |
| Species distribution modeling with Gaussian processes: A case study with the youngest stages of sea spawning whitefish (Coregonus lavaretus L. sl) larvae J Vanhatalo, L Veneranta, R Hudd Ecological Modelling 228, 49-58, 2012 | 34 | 2012 |
| By-catch of grey seals (Halichoerus grypus) in Baltic fisheries—A Bayesian analysis of interview survey J Vanhatalo, M Vetemaa, A Herrero, T Aho, R Tiilikainen PloS one 9 (11), e113836, 2014 | 32 | 2014 |
| Reproduction areas of sea-spawning coregonids reflect the environment in shallow coastal waters L Veneranta, R Hudd, J Vanhatalo Marine Ecology Progress Series 477, 231-250, 2013 | 30 | 2013 |
| Toward integrative management advice of water quality, oil spills, and fishery in the Gulf of Finland: A Bayesian approach M Rahikainen, I Helle, P Haapasaari, S Oinonen, S Kuikka, J Vanhatalo, ... Ambio 43 (1), 115-123, 2014 | 25 | 2014 |
Aki VehtariAssociate Professor, Aalto UniversityVerified email at aalto.fi
