We propose a series of Bayesian nonparametric statistical models for community detection in graphs. We model the probability of the presence or absence of edges within the graph. Using these models, ...

Bayesian nonparametric mixture models represent a powerful statistical framework that extends traditional mixture modelling by allowing the number of mixture components to be inferred from the data ...

This is a preview. Log in through your library . Abstract Efficiency scores of firms are measured by their distance to an estimated production frontier. The economic literature proposes several ...

Abstract: Many rivers in the Western U.S. suffer from high salinity content due to both natural and human-induced causes. Computer simulation models are often used to estimate future salinity levels ...

The team developed a non-parametric, model-free approach to quantify memory in complex systems such as soil, oceans, and ...

Non-parametric tests are used when standard assumptions are not available. These tests don’t rely on distributions, often ...