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Found 4 papers, 3 papers with code
Graph Neural Network-Based Anomaly Detection for River Network Systems
We further examine the strengths and weaknesses of this baseline approach, GDN, in comparison to other benchmarking methods on complex real-world river network data.
clusterBMA: Bayesian model averaging for clustering
In this work we introduce clusterBMA, a method that enables weighted model averaging across results from multiple unsupervised clustering algorithms.
On the intrinsic dimensionality of Covid-19 data: a global perspective
This paper aims to develop a global perspective of the complexity of the relationship between the standardised per-capita growth rate of Covid-19 cases, deaths, and the OxCGRT Covid-19 Stringency Index, a measure describing a country's stringency of lockdown policies.
Bayesian item response models for citizen science ecological data
We introduce a new methodological framework of item response and linear logistic test models with application to citizen science data used in ecology research.
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