A method and a processing device are provided for predicting standpipe pressure. A Bayesian linear regressor is initialized. Priors for the Bayesian linear regressor are initialized based on previous drilling operations that used a same bottom hole assembly. Measurement data associated with drilling a well is received in real time. An online Bayesian linear regressor update is generated using QR decomposition for a model. Responsive to determining that at least some coefficients violate physical rules, the at least some of the coefficients are set to a respective default value that is either zero or a positive value. Coefficients and uncertainty are updated based on at least one of the online Bayesian linear regressor update and the setting of at least some of the coefficients. The model is then visualized. Visualization helps a user identify whether the learned model makes sense.