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![]() Title:Extended Feature Allocation Models Conference:IMPMS 2026 Tags:Bayesian nonparametrics, Indian buffet process, Point processes and Sufficientness postulates Abstract: Feature allocation models are Bayesian nonparametric tools tailored to data in which each observation can simultaneously exhibit multiple characteristics, or features. A fundamental limitation of standard formulations, however, is that feature labels are assumed to be independent and identically distributed. This prevents the modeling of complex feature dependencies and ignores the predictive information carried by the observed labels. Extended Feature Allocation Models ![]() Extended Feature Allocation Models | ||||
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