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Jarno Vanhatalo
Jarno Vanhatalo
Associate professor, University of Helsinki
Verifierad e-postadress på helsinki.fi - Startsida
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GPstuff: Bayesian modeling with Gaussian processes
J Vanhatalo, J Riihimäki, J Hartikainen, P Jylänki, V Tolvanen, A Vehtari
The Journal of Machine Learning Research 14 (1), 1175-1179, 2013
342*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
2282019
Robust Gaussian Process Regression with a Student-t Likelihood.
P Jylänki, J Vanhatalo, A Vehtari
Journal of Machine Learning Research 12 (11), 2011
1772011
Gaussian process regression with Student-t likelihood
J Vanhatalo, P Jylänki, A Vehtari
Advances in neural information processing systems 22, 2009
1012009
Non-stationary Gaussian models with physical barriers
H Bakka, J Vanhatalo, JB Illian, D Simpson, H Rue
Spatial statistics 29, 268-288, 2019
84*2019
Approximate inference for disease mapping with sparse Gaussian processes
J Vanhatalo, V Pietiläinen, A Vehtari
Statistics in medicine 29 (15), 1580-1607, 2010
772010
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
692017
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
572017
Sparse log Gaussian processes via MCMC for spatial epidemiology
J Vanhatalo, A Vehtari
Gaussian processes in practice, 73-89, 2007
552007
Modelling local and global phenomena with sparse Gaussian processes
J Vanhatalo, A Vehtari
Proceedings of the 24th Conference on Uncertainty in Artificial Intelligence, 2008
51*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
502017
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
502017
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
492020
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
432019
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
422012
The value of reducing eutrophication in European marine areas—A Bayesian meta-analysis
H Ahtiainen, J Vanhatalo
Ecological Economics 83, 1-10, 2012
412012
Experiences in Bayesian inference in Baltic salmon management
S Kuikka, J Vanhatalo, H Pulkkinen, S Mäntyniemi, J Corander
372014
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
352014
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
322013
Making the most of mental models: Advancing the methodology for mental model elicitation and documentation with expert stakeholders
K LaMere, S Mäntyniemi, J Vanhatalo, P Haapasaari
Environmental modelling & software 124, 104589, 2020
282020
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Artiklar 1–20