Abstract: In this paper, we introduce a closed-form sparse Bayesian kernel Poisson regression (SBKPR) model for count data regression problems based on the sparse Bayesian learning (SBL) approach. In ...

Abstract: Poisson regression models the noisy output of a counting function as a Poisson random variable, with a log-mean parameter that is a linear function of the input vector. In this work, we ...

How Steve, a French cartoon with a catchy theme song, became a TikTok star. By Madison Malone Kircher If you have not yet heard the French song about an orange fish named Steve that is currently ...

This repository contains implementations of advanced regression methods, including ordinary least squares, Poisson regression, and locally weighted regression. It also explores bias-variance ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demo of Poisson regression, where the goal is to predict a count of things arriving, such as the number of telephone calls ...

This study presents the application of a new semi-analytical method of linear regression for Poisson count data to COVID-19 events. The regression is based on the maximum-likelihood solution for the ...

The Conway–Maxwell–Poisson (COMP) model is defined as a flexible count regression model used for over- and under-dispersion cases. In regression analysis, when ...