In this problem, the optimal path is one that maximizes the expected utility, with the utility function being piecewise-linear and concave. Such a utility function can be used to approximate nonlinear ...

This article reviews work on the planning of experiments with response functions nonlinear in some parameters. Apart from older work on dilution series experiments and quantal bioassays, this field is ...

While I do spend lots of time pushing the idea of linear approximations on my students, we do need nonlinear functions to describe most interesting physical phenomena.

This example solves a nonlinear system of equations by Newton's method. Let the nonlinear system be represented by F(x) = 0 ...

You construct a generalized linear model by deciding on response and explanatory variables for your data and choosing an appropriate link function and response probability distribution. Some examples ...

Linear regression is a statistical method used to understand the relationship between an outcome variable and one or more explanatory variables. It works by fitting a regression line through the ...

In this video, we will learn what is linear regression in machine learning along with examples to make the concept crystal clear.