Jarno Vanhatalo
Cited by
Cited by
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
Robust Gaussian Process Regression with a Student-t Likelihood.
P Jylänki, J Vanhatalo, A Vehtari
Journal of Machine Learning Research 12 (11), 2011
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
Gaussian process regression with Student-t likelihood
J Vanhatalo, P Jylänki, A Vehtari
Advances in neural information processing systems 22, 1910-1918, 2009
Approximate inference for disease mapping with sparse Gaussian processes
J Vanhatalo, V Pietiläinen, A Vehtari
Statistics in medicine 29 (15), 1580-1607, 2010
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
Non-stationary Gaussian models with physical barriers
H Bakka, J Vanhatalo, JB Illian, D Simpson, H Rue
Spatial statistics 29, 268-288, 2019
Sparse log Gaussian processes via MCMC for spatial epidemiology
J Vanhatalo, A Vehtari
Gaussian processes in practice, 73-89, 2007
Modelling local and global phenomena with sparse Gaussian processes
J Vanhatalo, A Vehtari
Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, 2008
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
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
The value of reducing eutrophication in European marine areas—A Bayesian meta-analysis
H Ahtiainen, J Vanhatalo
Ecological Economics 83, 1-10, 2012
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
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
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
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
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
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
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
Estimating the acute impacts of Arctic marine oil spills using expert elicitation
M Nevalainen, I Helle, J Vanhatalo
Marine pollution bulletin 131, 782-792, 2018
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