# Reliable predictions and forecasting: uncertainty quantification (risk assessment)

In real-world applications, one has to predict unknown values (disease risks, stocks or assets values, etc.) based on given information. Along with state-of-art methods, we develop novel algorithms that allow one to not only predict the desired values, but also estimate the certainty with which the predictions hold. All the methods are validated on real-world data sets.

Principle Investigators: Pavel Gurevich, Hannes Stuke

**Publications**

Gurevich P., Stuke H. Gradient conjugate priors and deep neural networks.Preprint: arXiv:1802.02643 [math.ST] (pdf) |

Gurevich P., Stuke H. Pairing an arbitrary regressor with an artificial neural network estimating aleatoric uncertainty. Neurocomputing. Accepted. (pdf) |