We assume that we have acquired some data D, this could be a set of pairs { f 2 , F 2 }, and a model M, e.g. the Mayo-Lewis equation which depends on parameters θ. In our case, θ symbolizes the reactivity ratios r 1 and r 2 . At the heart of parameter inference stands Bayes theorem. It relates the known objects: the distribution of the data given the parameters, p(D|θ) and the evidence p(D) to the desired quantity: the posterior p(θ|D). The Bayes theorem relates the two quantities by successive application of the definition of conditional probabilities: