8 citations · 8 across the 2 of their papers we have counts for
10 papers · 1 filter
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-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 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…