In generalized linear models, the response is assumed to possess a probability distribution of the exponential form. That is, the probability density of the response Y for continuous response ...

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 ...

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 ...

The convolutional neural network is also able to recover the true model variables (kernels) used to generate the probability of spiking in the output neuron. Based on the convolutional neural network ...

This paper investigates and questions the suitability of modelling non-linear loudspeaker distortion with scalar diagonal (SD) Volterra series. This approach, popular in studies of non-linear acoustic ...

I will present a General Linear Camera (GLC) model that unifies many previous camera models into a single representation. The GLC model describes all perspective (pinhole), orthographic, and many ...

Course TopicsOrdinary linear regression (OLR) assumes that response variables are continuous. Generalized Linear Models (GLMs) provide an extension to OLR since response variables can be discrete (e.g ...

Using this insight, we develop new invertible transformations named convolution exponentials and graph convolution exponentials, which retain the equivariance of their underlying transformations. In ...