(sec:RootScientificModeling)=
# Overview of scientific modeling material

The material here supplements the Bayesian methods for scientific modeling introduced in {ref}`part:Basics` with a more traditional viewpoint.
* {ref}`sec:OverviewModeling` address scientific models in general and regression analysis in particular. Comparisons are made between parametric and non-parametric models and between linear and non-linear models.
* {ref}`sec:LinearModels` gives a complete treatment of conventional (as opposed to Bayesian) linear regression.
* {ref}`sec:MathematicalOptimization` describes optimization schemes, which are of particular interest for minimizing loss functions of neural networks. In particular, gradient descent is covered in some detail, including stochastic and adaptive algorithms.