---
jupytext:
  formats: md:myst
  text_representation:
    extension: .md
    format_name: myst
  name: python3
---

(sec:ErrorPropagation)=
# Error propagation

In Bayesian statistics, error propagation is the process of determining how uncertainties in input parameters, which are characterized by probability distributions, influence the uncertainty (again, as a PDF) in the final output of a model. 
In general, Bayesian methods use the full PDFs to propagate uncertainty, in contrast to frequentist approaches.
In this chapter we look at three aspects of error propagation plus solutions to exercises:
* {ref}`sec:ErrorPropagationI`
* {ref}`sec:BayesianAdvantages:ChangingVariables`
* {ref}`sec:ErrorPropagationIII`
* {ref}`sec:ErrorPropagationSolutions`