Visual Studio Magazine

Linear Regression with Pseudo-Inverse Training Using JavaScript

Linear regression is the most fundamental machine learning technique to create a model that predicts a single numeric value. One of the three most common techniques to train a linear regression model ...
GitHub

Linear Regression Practice Using Python

Linear Regression Practice Using Python This study involves practical application of regression analysis, as covered in the Google Advanced Data Analytics course. For more information, you may refer ...
IEEE

Restricted Bayesian Lasso Regression With Inequality Constraints

Abstract: In this article, we investigate the utilization of the restricted Bayesian lasso regression, focusing on high-dimensional models that incorporate linear inequality constraints on the ...
Scientific Research Publishing

Gelman, A., Goodrich, B., Gabry, J. and Vehtari, A. (2019) R-Squared for Bayesian Regression Models. The American Statistician, 73, 307-309.

ABSTRACT: This research evaluates the effect of monetary policy rate and exchange rate on inflation across continents using both Frequentist and Bayesian Generalized Additive Mixed Models (GAMMs).
Visual Studio Magazine

Linear Regression Using JavaScript

Dr. James McCaffrey presents a complete end-to-end demonstration of linear regression using JavaScript. Linear regression is the simplest machine learning technique to predict a single numeric value, ...
Hosted on MSN

Linear Regression In Python From Scratch | Simply Explained

Implement Linear Regression in Python from Scratch ! In this video, we will implement linear regression in python from scratch. We will not use any build in models, but we will understand the code ...
IEEE

Bayesian Sequential Optimal Experimental Design for Linear Regression with Reinforcement Learning

Abstract: We perform a comparison study on Bayesian sequential optimal experimental design algorithms applied to linear regression in two unknowns. We transform the Bayesian sequential optimal ...
Scientific Research Publishing

Hahn, P.R., Murray, J.S. and Carvalho, C.M. (2020) Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects (with Discussion ...

ABSTRACT: This study explores the application of Bayesian econometrics in policy evaluation through theoretical analysis. The research first reviews the theoretical foundations of Bayesian methods, ...