Discrete approximation of multivariate densities with application to Bayesian estimation

A procedure is presented for finding a discrete approximation to a continuous multivariate density function. It is based on a previously developed algorithm [2] for determining the L₁ optimal discrete approximation to a univariate density. Results of approximating continuous bivariate density functions, which represent distributions of the parameters of a pharmacokinetic model, show good agreement between the mean and covariance matrix of the approximated and approximating densities. The distribution of a predicted drug conceptration was also calculated using a continuous density and discrete approximations with both 25 and 81 points. The expected values of the predicted concentration, as well as selected percentile points, obtained using each density are in close agreement.