BHPMF – a hierarchical Bayesian approach to gap-filling and trait prediction for macroecology and functional biogeography

By F. Schrodt, Jens Kattge, Hanhuai Shan, F. Fazayeli, Julia S. Joswig, Arindam Banerjee, Markus Reichstein, G. Bönisch, Sandra Díaz, John Dickie and 8 more. 2015

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How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI (2019, neutral): “The studies in [375–382] were carried out in diverse ﬁelds, showcasing the potential of this new paradigm for data science.”
Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data (2016, background): “This approach has been shown to provide significant improvements in the accuracy of predicting plant traits over traditional methods [47].”
Climate change vulnerability assessment of species (2018, background): “Progress towards imputing unknown trait data also helps to fill data gaps (Penone et al., 2014; Schrodt et al., 2015).”
Big data for forecasting the impacts of global change on plant communities (2017, methodology): “The community is implementing methods to resolve biases (Sandel et al. , 2015) and improve coverage via data imputation and machine learning techniques tailored to the characteristics of trait databases, such as trait correlations and phylogenetic signals (Schrodt et al. , 2015).”
Handling missing values in trait data (2020, methodology): “…methods available from three R packages (R v.3.5.0, R Core Team, 2018): (a) BHPMF, Bayesian hierarchal probabilistic matrix factorization (Schrodt et al., 2015); (b) Rphylopars (Goolsby et al., 2017); and (c) Mice, multiple imputation chained equations (Van Buuren & Groothuis-Oudshoorn,…”