Themen für Masterarbeiten
- Understanding and implementing predictive information criteria for Bayesian models
- Asymptotic behavior of the posterior in overfitted (deep) mixture models
- Posterior concentration rates for Bayesian high-dimensional sparse additive models
- Uncertainty quantification for deep learning
- Using stacking to average distributional regression models
- Measuring explained variance in structured additive distributional regression
- Comparisons and implementation of non-local shrinkage priors
- Approximated Bayesian multivariate spatial factor analysis
- Targeted sampling based on epistemic uncertainty for improving predictive performance in DL tasks with costly data acquisition
- Uncertainty quantification for complex-valued deep neural networks
- Variational inference for distributional regression
- Informed shrinkage in Bayesian graphical modeling via external networks
- Basis selection in Bayesian semi-parametric regression using increasing shrinkage priors
Kontakt: Prof. Dr. Nadja Klein