---
myst:
    substitutions:
        sentence1: ". For more details see {{metho}}"
---
```{include} /../core/calibration/abc.md
```

From a technical perspective, the methods in this section inherit from the `TABC` class (which 
itself inherits from `TCalibration`, in order to benefit from all standard 
features). Currently, the only implemented ABC method is the Rejection algorithm, presented in {{metho}}, whose 
implementation is provided through the `TRejectionABC` class described below.

The usage of the `TRejectionABC` class can be summarised in a few key steps:

1. Prepare the data and the model:
   - The parameters to be calibrated must be instances of classes inheriting from `TStochasticAttribute`;
   - Select the assessor type and construct the `TRejectionABC` object with the appropriate distance function (see [](#calibration_abc_construct_rejection_abc)).

2. Set the algorithm properties:
   - Define optional behaviours;
   - Specify the uncertainty hypotheses via the dedicated methods (see [](#calibration_abc_algo_properties)).

3. Perform the estimate and analyse the results:
   - Run the estimate process;
   - Extract the results and visualise them with the standard plotting tools (see [](#calibration_abc_results)).

```{toctree}
abc/construct_rejection_abc
abc/algo_properties
abc/results
```

An example is also provided in the use-case section (see [](#use_cases_macro_calibration_abc)).