Scale data before precision estimation

There can be a large difference between
```python
Prec_u_sub = fit_precision_cholesky(X, graph_u_sub, verbose_level=5)
```

and
```python
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler()

# Fit the scaler to the data and transform it
X_scaled = scaler.fit_transform(X)
X_scaled.shape
Prec_u_sub = fit_precision_cholesky(X_scaled, graph_u_sub, verbose_level=5)
```

In particular, this is experienced on e.g. `TOP_VOLANTIS` on Drogon. So it is a real issue.
The differences are in numerical stability for the optimization, and consequently large timing differences.

Remedy:
scale the data with the `StandardScaler` and then rescale either data or precision appropriately.
Likely:
- `fit_transform` data
- fit precision++
- transform / do the update
- `inverse_transform` the updated data