Duration: 12h. In this module, we will introduce generalized linear models (GLMs) through the study of binomial data. In particular, we will motivate the need for GLMs; introduce the binomial ...

Course TopicsIn many applications, the response variable is not Normally distributed. GLM can be used to analyze data from various non-Normal distributions. In this short course, we will introduce two ...

What is a Generalized Linear Model? A traditional linear model is of the form where y i is the response variable for the i th observation. The quantity x i is a column vector of covariates, or ...

GENMOD uses maximum likelihood estimation to fit generalized linear models. This family includes models for categorical data such as logistic, probit, and complementary log-log regression for binomial ...

An introduction to the theory and application of generalised linear models for the analysis of continuous, categorical and count data, and regression models for survival data. Topics include: general ...

The great breakthrough about this model is that it makes no assumption about input data type, while, for instance, existing convolutional neural networks work for images only. Source: Perceiver ...

Multiple regression and regression diagnostics. Generalised linear models; the exponential family, the linear predictor, link functions, analysis of deviance, parameter estimation, deviance residuals.

Researchers find large language models process diverse types of data, like different languages, audio inputs, images, etc., similarly to how humans reason about complex problems. Like humans, LLMs ...