Regression models with intractable normalizing constants are valuable tools for analyzing complex data structures, yet ...

Abstract: We consider covariance estimation in the multivariate generalized Gaussian distribution (MGGD ... These include, for example, maximum likelihood estimation of banded inverse covariances in ...

In this paper, we propose a novel path loss model based on multi-dimensional Gaussian process regression (GPR) that gives spatial consistency to channels in propagation environment by predicting local ...

effectively removing the corresponding predictors from the model. Lasso linear regression includes a penalty term to the standard least squares objective function. The penalty term is a sum of the ...

Predicting car prices using multiple linear regression. This project uses real-world automotive data to train a machine learning model capable of estimating car prices based on technical ...