LAUSR

Geostatistical models quantify spatial relations between model parameters and can be used to estimate and simulate properties away from known observations. The underlying statistical model, quantified through a joint probability density, most often consists of both an assumed statistical model and the specific choice of algorithm, including tuning parameters controlling the algorithm. Here, a theory is developed that allows one to compute the entropy of the underlying multivariate probability density when sampled using sequential simulation. The self-information of a single realization can be computed as the sum of the conditional self-information. The entropy is the average of the self-information obtained for many independent realizations. For discrete probability mass functions, a measure of the effective number of free model parameters, implied by a specific choice of probability mass function, is proposed. Through a few examples, the entropy measure is used to quantify the information content related to different choices of simulation algorithms and tuning parameters.