(calibration_abc_algo_properties_percentile)=
# Defining the percentile

The first method discussed here is straightforward: the principle of rejection is to keep the best-tested 
configurations. This can be done either by applying a threshold value on the distance results (called 
$\delta$ in {{metho}}) or by retaining a fixed fraction of the tested configurations, defined through a 
percentile $\varepsilon_{Dist}$. The `TRejectionABC` method implements the latter approach. By **default**, 
the percentile is set to $\varepsilon_{Dist} = 0.01$.

To modify this value, the user can call `setPercentile`, whose prototype is

````{only} cpp
```cpp
void setPercentile(double eps);
```
````

````{only} py
```python 
setPercentile(eps)
```
````

where the argument `eps` specifies the fraction of configurations to be kept.

An important consequence is that the total number of configurations evaluated is computed as follows:

```{math}
n_{\rm Comp} = \frac{n_{S}}{\varepsilon_{Dist}}
```

where $n_{S}$ is the number of retained samples in the final 
posterior distribution, as defined in the constructor (see [](#calibration_abc_construct_rejection_abc)).