# Prediction of the variance

Once the estimate is completed, it is possible to compute the central value for a new 
set of input values (i.e., for a new {{doe}}) using the newly estimated parameter values. 
Although this applies to every method in the calibration module, the 
Linear Bayesian procedure has the advantage of providing the covariance matrix of the parameters. Under some 
assumptions on the input distribution, it is possible to obtain a variance for each new predicted 
value, reflecting only the uncertainty due to the parameters. For more details on the 
estimate, see {{metho}}. This can be done by calling the 
`computePredictionVariance` method:

````{only} cpp
```cpp
void computePredictionVariance(URANIE::DataServer::TDataServer *tdsPred, string outname);
```
````

````{only} py
```python 
computePredictionVariance(tdsPred, outname)
```
````

This method takes two arguments, which are:

1. **tdsPred**: a {{tds}} containing the new locations to be estimated, in which **all** regressors must be 
available in order to be able to compute the covariance matrix;

2. **outname**: the name of the attribute to be created, which will be filled with the diagonal elements (the 
variances) of the $ \Sigma^{pred}_{\theta} $ matrix.