The prior represents beliefs or knowledge (such as f.e. physical constraints) about formula_1 before formula_2 is available. Since the prior narrows down uncertainity, the posterior estimates have less variance, but might be biased. For convenience the prior is often specified by choosing a particular distribution among a set of well-known and tractable families of distributions, such that both the evaluation of prior probabilities and random generation of values of formula_1 are relatively straightforward. For certain kinds of models, it is more pragmatic to specify the prior formula_8 using a factorization of the joint distribution of all the elements of formula_1 in terms of a sequence of their conditional distributions. If one is only interested in the relative posterior plausibilities of different values of formula_1, the evidence formula_9 can be ignored, as it constitutes a normalising constant, which cancels for any ratio of posterior probabilities. It remains, however, necessary to evaluate the likelihood formula_7 and the prior formula_8. For numerous applications, it is computationally expensive, or even completely infeasible, to evaluate the likelihood, which motivates the use of ABC to circumvent this issue.