---
myst:
    substitutions:
        sentence1: "The reference data"
        sentence2: "problem-dependent"
---
(calibration_intro)=
# Introduction

```{include} /../core/calibration/introduction.md
:start-line: 0
:end-line: 4
```

They are all gathered in the **libCalibration** module. The namespace 
of this library is **URANIE::Calibration**. Each technique discussed later on is 
theoretically introduced in {{metho}} along with a general discussion on calibration and particularly 
on its statistical interpretation.

```{include} /../core/calibration/introduction.md
:start-line: 5
:end-line: 15
```

The rest of this section introduces the available distances and likelihoods used to compare observations with model 
predictions, in [](#calibration_introduction_distance_compare_model) while the methods are discussed in 
their own sections. The already predefined calibration methods proposed in the {{uranie}} platform 
are listed below:

```{include} /../core/calibration/introduction.md
:start-line: 16
:end-line: 23
```

As for other modules, there is a specific class organisation that links the main classes in this 
module. The class hierarchy is shown in {numref}`calibration_Calibration_Class_Hierarchy` and is 
discussed a bit here to explain the two main classes from which all other classes are derived 
and the corresponding shared functions used throughout the methods. One can see this organisation with 
the two sets of classes: those inheriting from the `TCalibration` class and those inheriting from 
`TDistanceLikelihoodFunction` class. The former are the different methods that have been developed to calibrate 
a model with respect to the observations and each method will be discussed in the upcoming 
sections. Whatever the method under consideration, it always includes a distance or a likelihood function object, 
which belongs to the latter category and its main job is to quantify how close the model predictions 
are to the observations. These objects are discussed in the rest of this introduction, see for 
instance [](#calibration_introduction_distance_compare_model).

{{
    "```{" "figure" "} " + parent_dir +
        "/usermanual/calibration/figures/Calibration_Class_Hierarchy.png\n"
    ":align: center\n"
    ":name: calibration_Calibration_Class_Hierarchy\n"
    + figure_scale + "\n"
    "\n"
    "Hierarchy of classes and structures out of " + doxy + " for the **Calibration** module\n"
    "```"
}}

Although CIRCE is not strictly a calibration method, it has been included in this section because it 
relies on approaches presented here. Indeed, the idea behind this method is to quantify the uncertainty 
of a given quantity by multiplying it by a Gaussian random variable whose standard deviation must be 
calibrated. A final section therefore introduces this method.

```{include} /../core/calibration/introduction.md
:start-line: 24
```

The next section focuses on the distances and likelihoods already implemented in {{uranie}}, which can 
be used directly within the calibration methods.

```{toctree}
introduction/distance_compare_model
```