Deep neural networks (NNs) are known for their high-prediction performan...
This study aims to investigate the utilization of Bayesian techniques fo...
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 ...
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...
We propose a novel end-to-end neural network architecture that, once tra...