where P (θ) is called the prior distribution of θ and is chosen to capture any prior knowledge we have of θ before any experimental observation. The distribution P (y|θ) is the probability density of the experimental data y given a model parameterised with parameters θ, and is termed the likelihood of the data; assuming a known distribution of the error in the data this likelihood can be calculated. P (y) is a normalising term (which is the integral of all possible densities P (y, θ) = P (y|θ)P (θ) over all values of θ), and ensures that the posterior density P (θ|y) integrates to 1. In practice, the calculation of this normalising term (which can be very computationally expensive) is avoided by considering ratios of the likelihood (see below).