Deep neural networks (NNs) are known for their high-prediction performan...
Contemporary empirical applications frequently require flexible regressi...
For many medical applications, interpretable models with a high predicti...
The transition to a fully renewable energy grid requires better forecast...
Variational inference (VI) is a technique to approximate difficult to co...
The main challenge in Bayesian models is to determine the posterior for ...
Prediction models often fail if train and test data do not stem from the...
Outcomes with a natural order commonly occur in prediction tasks and
oft...
At present, the majority of the proposed Deep Learning (DL) methods prov...
Deep neural networks (DNNs) are known for their high prediction performa...
We present a deep transformation model for probabilistic regression. Dee...