The main focus of this short course will be the Bayesian aspect of it. That means this is a slightly more advanced course requiring some knowledge of basic probability, regression methods, and the R ...

We propose a nested Gaussian process (nGP) as a locally adaptive prior for Bayesian nonparametric regression. Specified through a set of stochastic differential equations (SDEs), the nGP imposes a ...

Some of you may have come across a growing number of publications in your field using an alternative paradigm called Bayesian statistics in which to perform their statistical analyses. The goal of ...

The goal of a machine learning regression problem is to predict a single numeric value. There are roughly a dozen different regression techniques such as basic linear regression, k-nearest neighbors ...

This is a preview. Log in through your library . Abstract Ecosystems sometimes undergo dramatic shifts between contrasting regimes. Shallow lakes, for instance, can transition between two alternative ...

The least absolute shrinkage and selection operator (Lasso) estimation of regression coefficients can be expressed as Bayesian posterior mode estimation of the regression coefficients under various ...