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