Common statistical usage defines precision as the reciprocal of the variance, and the precision matrix as the matrix inverse of the covariance matrix. Some particular statistical models define the term precision differently. One particular use of the precision matrix is in the context of Bayesian analysis of the multivariate normal distribution: for example, Bernardo & Smith prefer to parameterise the multivariate normal distribution in terms of the precision matrix, rather than the covariance matrix, because of certain simplifications that then arise.