Stable Training of Probabilistic Models Using the Leave-One-Out Maximum Log-Likelihood Objective

arXiv:2310.03556v2 Announce Type: replace-cross
Abstract: Probabilistic modelling of power systems operation and planning processes depends on data-driven methods, which require sufficiently large datasets. When historical data lacks this, it is desired to model the underlying data generation mechanism as a probability distribution to assess the data quality and generate more data, if needed. Kernel density estimation (KDE) based models are popular choices for this task, but they fail to adapt to data regions with varying densities. In this paper, an …

abstract arxiv cs.lg cs.sy data data-driven data quality datasets distribution eess.sy generate historical data large datasets leave-one-out likelihood modelling planning power probability processes quality stat.ml systems training type