35 citations · 153 across the 16 of their papers we have counts for
18 papers
On Asymptotic Outlier Rejection in Bayesian Mixed Poisson Regression Models Under Extreme Target and Covariate Values
Ilaria Pia, Jarno Vanhatalo
Bayesian models are defined to be fully robust against outliers if observations infinitely far from the other data do not influence the posterior. In regression models, this entail…
Bayesian Calibration and Uncertainty Quantification for a Large Nutrient Load Impact Model
Karel Kaurila, Risto Lignell, Frede Thingstad +2
Nutrient load simulators are large, deterministic, models that simulate the hydrodynamics and biogeochemical processes in aquatic ecosystems. They are central tools for planning co…
Integrated population model reveals human and environment driven changes in Baltic ringed seal (Pusa hispida botnica) demography and behavior
Murat Ersalman, Mervi Kunnasranta, Markus Ahola +6
Integrated population models (IPMs) are a promising approach to test ecological theories and assess wildlife populations in dynamic and uncertain conditions. By combining multiple…
Species Distribution Modeling with Expert Elicitation and Bayesian Calibration
Karel Kaurila, Sanna Kuningas, Antti Lappalainen +1
Species distribution models (SDMs) are key tools in ecology, conservation and management of natural resources. They are commonly trained by scientific survey data but, since survey…
Joint Species Distribution Modeling of Percentage Cover Data with Exclusive Competition for Space
Juho Kettunen, Lauri Mehtätalo, Eeva-Stiina Tuittila +2
Joint species distribution models (JSDM) are among the most important statistical tools in community ecology. They are routinely used for inference and various prediction tasks, su…
EcoMem: An R package for quantifying ecological memory
Malcolm S. Itter, Jarno Vanhatalo, Andrew O. Finley
Ecological processes may exhibit memory to past disturbances affecting the resilience of ecosystems to future disturbance. Understanding the role of ecological memory in shaping ec…
Additive multivariate Gaussian processes for joint species distribution modeling with heterogeneous data
Jarno Vanhatalo, Marcelo Hartmann, Lari Veneranta
Species distribution models (SDM) are a key tool in ecology, conservation and management of natural resources. Two key components of the state-of-the-art SDMs are the description f…
Bayesian model-based spatiotemporal survey design for log-Gaussian Cox process
Jia Liu, Jarno Vanhatalo
In geostatistics, the design for data collection is central for accurate prediction and parameter inference. One important class of geostatistical models is log-Gaussian Cox proces…
Laplace approximation and the natural gradient for Gaussian process regression with the heteroscedastic Student-t model
Marcelo Hartmann, Jarno Vanhatalo
This paper considers the Laplace method to derive approximate inference for the Gaussian process (GP) regression in the location and scale parameters of the Student-t probabilistic…
Correcting boundary over-exploration deficiencies in Bayesian optimization with virtual derivative sign observations
Eero Siivola, Aki Vehtari, Jarno Vanhatalo +2
Bayesian optimization (BO) is a global optimization strategy designed to find the minimum of an expensive black-box function, typically defined on a compact subset of $\mathcal{R}^…
Optimal design of observational studies: overview and synthesis
Juha Karvanen, Jarno Vanhatalo, Kari Auranen +2
We review typical design problems encountered in the planning of observational studies and propose a unifying framework that allows us to use the same concepts and notation for dif…
Non-stationary Gaussian models with physical barriers
Haakon Bakka, Jarno Vanhatalo, Janine Illian +2
The classical tools in spatial statistics are stationary models, like the Matérn field. However, in some applications there are boundaries, holes, or physical barriers in the study…
A Bayesian length-based population dynamics model for northern shrimp (Pandalus Borealis)
Paul Blomstedt, Jarno Vanhatalo, Mats Ulmestrand +2
We introduce a fully length-based Bayesian model for the population dynamics of northern shrimp (Pandalus Borealis). This has the advantage of structuring the population in terms o…
Experiences in Bayesian Inference in Baltic Salmon Management
Sakari Kuikka, Jarno Vanhatalo, Henni Pulkkinen +2
We review a success story regarding Bayesian inference in fisheries management in the Baltic Sea. The management of salmon fisheries is currently based on the results of a complex…
Modelling local and global phenomena with sparse Gaussian processes
Jarno Vanhatalo, Aki Vehtari
Much recent work has concerned sparse approximations to speed up the Gaussian process regression from the unfavorable O(n3) scaling in computational time to O(nm2). Thus far, work…
Bayesian Modeling with Gaussian Processes using the GPstuff Toolbox
Jarno Vanhatalo, Jaakko Riihimäki, Jouni Hartikainen +3
Gaussian processes (GP) are powerful tools for probabilistic modeling purposes. They can be used to define prior distributions over latent functions in hierarchical Bayesian models…
Speeding up the binary Gaussian process classification
Jarno Vanhatalo, Aki Vehtari
Gaussian processes (GP) are attractive building blocks for many probabilistic models. Their drawbacks, however, are the rapidly increasing inference time and memory requirement alo…
Gaussian Process Regression with a Student-t Likelihood
Pasi Jylänki, Jarno Vanhatalo, Aki Vehtari
This paper considers the robust and efficient implementation of Gaussian process regression with a Student-t observation model. The challenge with the Student-t model is the analyt…