11 citations · 16 across the 9 of their papers we have counts for
9 papers
Bayesian structured additive quantile regression for inflated bounded data
Francisco F. Queiroz, Johannes Brachem, Paul F. V. Wiemann +1
Bounded continuous data on the unit interval frequently arise in applied fields and often exhibit a non-negligible proportion of observations at the boundaries. Inflated regression…
Data-Efficient Generative Modeling of Non-Gaussian Global Climate Fields via Scalable Composite Transformations
Johannes Brachem, Paul F. V. Wiemann, Matthias Katzfuss
Quantifying uncertainty in climate-model output requires characterizing internal variability, often through large ensembles of physical climate-model runs. Since each additional en…
Generative multi-scale modeling via spatial autoregressive transport maps
Alejandro Calle-Saldarriaga, Paul F. V. Wiemann, Matthias Katzfuss
Spatial fields in the Earth and environmental sciences are often available at multiple scales or resolutions. While coarse-scale data (e.g., from global circulation models) are oft…
Stochastic Variational Inference for Structured Additive Distributional Regression
Gianmarco Callegher, Thomas Kneib, Johannes Söding +1
Structured additive distributional regression extends generalized additive models by allowing all parameters of a response distribution to depend on structured additive predictors.…
Bayesian Penalized Transformation Models: Structured Additive Location-Scale Regression for Arbitrary Conditional Distributions
Johannes Brachem, Paul F. V. Wiemann, Thomas Kneib
Penalized transformation models (PTMs) are a semiparametric location-scale regression family that estimate a response's conditional distribution directly from the data, and model t…
Bayesian nonparametric generative modeling of large multivariate non-Gaussian spatial fields
Paul F. V. Wiemann, Matthias Katzfuss
Multivariate spatial fields are of interest in many applications, including climate model emulation. Not only can the marginal spatial fields be subject to nonstationarity, but the…
Bayesian spatial+: A joint model perspective
Isa Marques, Paul F. V. Wiemann
Spatial confounding is a common issue in spatial regression models, occurring when spatially varying covariates correlate with the spatial effect included in the model. This depend…
Liesel: A Python Framework for Graph-Based Bayesian Modeling and Customizable MCMC with Support for Generalized Additive Models
Hannes Riebl, Johannes Brachem, Thomas Kneib +2
Liesel is a Python framework for Bayesian model building and posterior computation with dedicated support for generalized additive regression models that is designed to reduce fric…
Using the Softplus Function to Construct Alternative Link Functions in Generalized Linear Models and Beyond
Paul F. V. Wiemann, Thomas Kneib, Julien Hambuckers
Response functions linking regression predictors to properties of the response distribution are fundamental components in many statistical models. However, the choice of these func…