Bayesian statistical techniques can be leveraged to combine previous knowledge (previous data, values from literature, modeller’s experience) with new data to estimate model parameters. This report shows Bayesian objective functions to allow incorporation of prior knowledge, demonstrated on a steady-state gas-phase n-hexane hydroisomerization case study. Knowledge about an old catalyst obtained from a large dataset is transferred in different ways to estimate kinetic parameters for a new, but similar catalyst. Three scenarios using small datasets for the new catalyst are considered. The results show that intelligent priors from reliable knowledge is useful if the dataset for the new catalyst is limited, but not with larger datasets and particularly not with priors based on mistaken beliefs.