More Like this

1. The generalized Poisson-binomial distribution and the computation of its distribution function

   https://doi.org/10.1080/00949655.2018.1440294  

   Zhang, Man; Hong, Yili; Balakrishnan, Narayanaswamy (February 2018, Journal of Statistical Computation and Simulation)
2. Stabilizing the Kumaraswamy Distribution

   Wasserman, Max; Mateos, Gonzalo (March 2025, Transactions on machine learning research)
3. The Distribution of Species Interactions

   https://doi.org/10.1086/727939  

   Karban, Richard; Agrawal, Anurag A. (December 2023, The Quarterly Review of Biology)
4. Changing the Training Data Distribution to Reduce Simplicity Bias Improves In-distribution Generalization

   Nguyen, Dang; Haddad, Paymon; Gan, Eric; Mirzasoleiman, Baharan (December 2024, Advances in Neural Information Processing Systems (NeurIPS))
5. The influence of the number and distribution of background points in presence-background species distribution models

   https://doi.org/10.1016/j.ecolmodel.2023.110604  

   Whitford, Anna M.; Shipley, Benjamin R.; McGuire, Jenny L. (February 2024, Ecological Modelling)

   Species distribution models (SDMs), which relate recorded observations (presences) and absences or background points to environmental characteristics, are powerful tools used to generate hypotheses about the biogeography, ecology, and conservation of species. Although many researchers have examined the effects of presence and background point distributions on model outputs, they have not systematically evaluated the effects of various methods of background point sampling on the performance of a single model algorithm across many species. Therefore, a consensus on the preferred methods of background point sampling is lacking. Here, we conducted presence-background SDMs for 20 vertebrate species in North America under a variety of background point conditions, varying the number of background points used, the size of the buffer used to constrain the background points around the occurrences, and the percentage of background points sampled within the buffer (“spatial weighting”). We evaluated the accuracy and transferability of the models using Boyce index, overlap with expert-generated range maps, and area overpredicted and underpredicted by the SDM (and AUC for comparability with other studies). SDM performance is highly dependent on the species modelled but is affected by the number and spread of background points. Models with little spatial weighting had high accuracy (overlap values), but extreme extrapolation errors and overprediction. In contrast, SDMs with high transferability (high Boyce index values and low overprediction) had moderate-to-high spatial weighting. These results emphasize the importance of both background points and evaluation metric selection in SDMs. For other, more successful metrics, using many background points with spatial weighting may be preferred for models with large extents. These results can assist researchers in selecting the background point parameters most relevant for their research question, allowing them to fine-tune their hypotheses on the distribution of species through space and time. 

   more » « less