(calibration_linear_bayesian_linear_model)=
# Defining the `TLinearBayesian` properties

Once the `TLinearBayesian` instance is created along with its `TDistanceLikelihoodFunction`, two methods must 
be called before performing parameter estimate. These methods are mandatory, as they define the 
analytical formula used to obtain the Gaussian parameter values of the *a posteriori* 
distribution (see {{metho}}).

The first method (although the order is not important) is `setRegressorName`, whose 
prototype is

````{only} cpp
```cpp
void setRegressorName(const char *regressorname);
```
````

````{only} py
```python 
setRegressorName(regressorname)
```
````

The only argument is `regressorname`, a string containing the list of regressor names separated by ":". 
The method then performs two checks: it verifies that the 
number of regressors matches the number of parameters to be calibrated and it checks that 
every regressor name provided matches one existing attribute in the reference {{tds}} (`tdsref`). 
If the observation 
{{tds}} does not contain the regressors (when the input file is loaded) these attributes must be 
constructed from scratch, either with `TAttributeFormula` or by using another dedicated assessor 
(as done in the use-case shown in [](#use_cases_macro_calibration_linBayes)).

The other method is `setObservationCovarianceMatrix`, whose prototype is

````{only} cpp
```cpp
void setObservationCovarianceMatrix(TMatrixD &mat);
```
````

````{only} py
```python
setObservationCovarianceMatrix(mat)
```
````

The only argument here is a `TMatrixD` whose content is the covariance matrix of the reference 
observation data. Once again, this method will check two things:

- the provided matrix must have the correct dimensions (both rows and columns must be equal to $n$);

- the provided matrix should be symmetrical;

Once these conditions are satisfied, estimate can proceed. One can find an example of how to use these methods in the 
use-case dedicated subsection (see [](#use_cases_macro_calibration_linBayes)).