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Found 4 papers, 1 papers with code
Multi-variable Hard Physical Constraints for Climate Model Downscaling
Global Climate Models (GCMs) are the primary tool to simulate climate evolution and assess the impacts of climate change.
Using Explainability to Inform Statistical Downscaling Based on Deep Learning Beyond Standard Validation Approaches
Deep learning (DL) has emerged as a promising tool to downscale climate projections at regional-to-local scales from large-scale atmospheric fields following the perfect-prognosis (PP) approach.
Learning complex dependency structure of gene regulatory networks from high dimensional micro-array data with Gaussian Bayesian networks
Gene expression datasets consist of thousand of genes with relatively small samplesizes (i. e. are large-$p$-small-$n$).
Who Learns Better Bayesian Network Structures: Accuracy and Speed of Structure Learning Algorithms
Three classes of algorithms to learn the structure of Bayesian networks from data are common in the literature: constraint-based algorithms, which use conditional independence tests to learn the dependence structure of the data; score-based algorithms, which use goodness-of-fit scores as objective functions to maximise; and hybrid algorithms that combine both approaches.
