80 citations · 127 across the 12 of their papers we have counts for
15 papers
Generalised logistic regression with vine copulas
Ingrid Hobæk Haff, Simon Boge Brant, Haakon Bakka
We propose a generalisation of the logistic regression model, that aims to account for non-linear main effects and complex interactions, while keeping the model inherently explaina…
High stakes classification with multiple unknown classes based on imperfect data
Haakon Bakka
High stakes classification refers to classification problems where erroneously predicting the wrong class is very bad, but assigning "unknown" is acceptable. We make the argument t…
High-resolution Bayesian mapping of landslide hazard with unobserved trigger event
Thomas Opitz, Haakon Bakka, Raphaël Huser +1
Statistical models for landslide hazard enable mapping of risk factors and landslide occurrence intensity by using geomorphological covariates available at high spatial resolution.…
A diffusion-based spatio-temporal extension of Gaussian Matérn fields
Finn Lindgren, Haakon Bakka, David Bolin +2
Gaussian random fields with Matérn covariance functions are popular models in spatial statistics and machine learning. In this work, we develop a spatio-temporal extension of the G…
A principled distance-based prior for the shape of the Weibull model
Janet van Niekerk, Haakon Bakka, Haavard Rue
The use of flat or weakly informative priors is popular due to the objective a priori belief in the absence of strong prior information. In the case of the Weibull model the improp…
Competing risks joint models using R-INLA
Janet van Niekerk, Haakon Bakka, Haavard Rue
The methodological advancements made in the field of joint models are numerous. None the less, the case of competing risks joint models have largely been neglected, especially from…
New frontiers in Bayesian modeling using the INLA package in R
Janet van Niekerk, Haakon Bakka, Haavard Rue +1
The INLA package provides a tool for computationally efficient Bayesian modeling and inference for various widely used models, more formally the class of latent Gaussian models. It…
Max-and-Smooth: a two-step approach for approximate Bayesian inference in latent Gaussian models
Birgir Hrafnkelsson, Stefan Siegert, Raphaël Huser +2
With modern high-dimensional data, complex statistical models are necessary, requiring computationally feasible inference schemes. We introduce Max-and-Smooth, an approximate Bayes…
Approximate Bayesian inference for analysis of spatio-temporal flood frequency data
Árni V. Johannesson, Stefan Siegert, Raphaël Huser +2
Extreme floods cause casualties, and widespread damage to property and vital civil infrastructure. We here propose a Bayesian approach for predicting extreme floods using the gener…
Joint models as latent Gaussian models - not reinventing the wheel
Janet Van Niekerk, Haakon Bakka, Haavard Rue
Joint models have received increasing attention during recent years with extensions into various directions; numerous hazard functions, different association structures, linear and…
Geostatistical modeling to capture seismic-shaking patterns from earthquake-induced landslides
Luigi Lombardo, Haakon Bakka, Hakan Tanyas +3
In this paper, we investigate earthquake-induced landslides using a geostatistical model that includes a latent spatial effect (LSE). The LSE represents the spatially structured re…
How to solve the stochastic partial differential equation that gives a Matérn random field using the finite element method
Haakon Bakka
This tutorial teaches parts of the finite element method (FEM), and solves a stochastic partial differential equation (SPDE). The contents herein are considered "known" in the nume…
INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles
Thomas Opitz, Raphaël Huser, Haakon Bakka +1
This work has been motivated by the challenge of the 2017 conference on Extreme-Value Analysis (EVA2017), with the goal of predicting daily precipitation quantiles at the …
Spatial modelling with R-INLA: A review
Haakon Bakka, Håvard Rue, Geir-Arne Fuglstad +5
Coming up with Bayesian models for spatial data is easy, but performing inference with them can be challenging. Writing fast inference code for a complex spatial model with realist…
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…