These types of situations can often be modeled well by a large class of regression models called generalized linear models (GLM ... such as R and SAS (possibly JMP if time allows) and explain how we ...

Ordinary linear regression (OLR) assumes that response variables are continuous. Generalized ... real data and explain how we will interpret some of the output from the software. There will be several ...

Predictive microbiology models explain bacterial number variations over time and ... between variability and uncertainty has been pointed out (Nauta, 2000). The generalized linear model (GLM) is an ...

Abstract: Supervised learning over graphs is an intrinsically difficult problem ... We present a direct sparse optimization algorithm for generalized problems with arbitrary twice-differentiable loss ...

Methods: In this paper, we first demonstrate a method of modeling these joint statistics with a flexible parametric approach, where we model the conditional distribution of amplitude given phase using ...

To establish the consistency of DARLS, we also derive new identifiability results for causal graphs parameterized by generalized linear models, which could be of independent interest. Through ...

This README.md file is to specify the order of "difficulty" for the repositories, where difficulty is measured by number of models and different distributions in order to explain the data ... is what ...