This manuscript provides valuable information on the neurodynamics of emotional processing while participants were watching movie clips. The methods and results were solid in deciphering the ...

Ordinary vs Bayesian linear regression. Linear regression vs logistic regression. ... linear regression has become a fundamental machine learning algorithm and can be particularly useful for complex ...

In this article, a sparse signal recovery algorithm using Bayesian linear regression with Cauchy prior (BLRC) is proposed. Utilizing an approximate expectation maximization (AEM) scheme, a systematic ...

Nonlinear regression algorithms, which fit curves that are not linear in their parameters to data, are a little more complicated, because, unlike linear regression problems, they can’t be solved ...

The relative importance of the predictors can be estimated by the linear regression of the set of coefficients. This statement can also be considered as the basic approach of the MMMs. Talking about ...