44 research outputs found
Urysohn Forest for Aleatoric Uncertainty Quantification
This paper focuses on building models of stochastic systems with aleatoric
uncertainty. The main novelty is an algorithm of boosted ensemble training of
multiple models for obtaining a probability distribution of an individual
output as a function of the system input. The second novel contribution is a
new regression model to be used in the ensemble. The model is a multi-layered
tree of hierarchically-connected discrete Urysohn operators (or generalised
additive models, which are mathematically equivalent to the discrete Urysohn
operators in this case). Since multiple models (trees) are trained in the
ensemble, the authors refer them as an Urysohn forest. The source code is
freely available online