It is typically assumed that large networks of neurons exhibit a large repertoire of nonlinear behaviours. Here we challenge this assumption by leveraging mathematical models derived from measurements ...

Learn how nonlinear and linear regression models differ, predict variables, and their applications in data analysis for ...

Dimensionality reduction is a method used in machine learning and data science to reduce the dimensions in a dataset. While linear methods are generally less effective at dimensionality reduction than ...

The actuarial methodology powering insurance risk models is advancing faster than most carriers realize. Here is what is ...

Statistical modeling lies at the heart of data science. Well-crafted statistical models allow data scientists to draw conclusions about the world from the limited information present in their data. In ...

Microbiome sequencing data are known to be biased; the measured taxa relative abundances can be systematically distorted from their true values at every step in the experimental/analysis workflow. If ...