Components#
This tutorials introduce in more depth specific components of zfit.
05 FitResult#
13 Kernel Density Estimation#
20 Composite Models#
Building models out of other models using sums, products and more is an essential part of model building. This tutorial starts with the basics of it.
30 Binned Models#
Binned models and data handle differently than their unbinned counterparts.
40 Bayesian Inference#
Bayesian inference is a powerful tool to infer parameters given data and a model.
50 Custom code and different run modes#
60 Custom PDF#
Being able to build a custom model simply is an essential feature of zfit. This tutorial introduces the two main ways of doing it, a simpler and a more advanced, more flexible way.
61 Custom Binned PDF#
Building a binned pdf in zfit.
62 Multidimensional custom PDF#
Building a pdf in multiple dimensions and registering an analytic integral.
71 - Simple Loss#
A simple loss doesn’t need a distribution or data, it just needs a function to minimize.
72 - Custom Loss#
Building a custom loss function in zfit.
77 - Custom Minimizers#
Building a custom minimizer in zfit.
80 Toy Study#
A minimal example of how to manually perform toy studies with zfit.
90 Serialization#
There are multiple ways of serializing zfit objects, this tutorial introduces them.
Warning
Parts of it, namely the HS3-like human-readable serialization is still highly experimental and will change in every release.
If any component is missing, please open an issue on github.