Co-data Learning for Bayesian Additive Regression Trees

1 code implementation • 16 Nov 2023 • Jeroen M. Goedhart, Thomas Klausch, Jurriaan Janssen, Mark A. van de Wiel

To address these challenges, we propose to incorporate co-data, i. e. external information on the covariates, into Bayesian additive regression trees (BART), a sum-of-trees prediction model that utilizes priors on the tree parameters to prevent overfitting.

regression Variable Selection

Estimation of Predictive Performance in High-Dimensional Data Settings using Learning Curves

no code implementations • 8 Jun 2022 • Jeroen M. Goedhart, Thomas Klausch, Mark A. van de Wiel

To address this challenge, a novel performance estimation framework is presented.

Estimating Bayesian Optimal Treatment Regimes for Dichotomous Outcomes using Observational Data

no code implementations • 18 Sep 2018 • Thomas Klausch, Peter van de Ven, Tim van de Brug, Mark A. van de Wiel, Johannes Berkhof

We discuss Bayes optimal treatment regimes estimated using a loss function defined on the bivariate distribution of dichotomous potential outcomes.

Bayesian Inference