ESTIMATING EARTH’S UNDISCOVERED, MINERALOGICAL DIVERSITY USING A BAYESIAN APPROACH

A Bayesian approach is introduced to estimate the total number of mineral species in Earth’s crust. In Bayesian statistics, the parameters are not considered fixed as they are in classical statistics but assumed to have their own probability distribution such that prior information about the parameters can be built into the model. Samples of the model parameters are generated by Markov chain Monte Carlo (MCMC) simulations and used for estimates and inference. Species accumulation curves are constructed and employed to estimate the population size as a function of sampling size. The Poisson lognormal distribution is found to provide the best fit to the mineral species frequency spectrum among the distributions tested. Finally, the population size estimates obtained by Bayesian methods are compared to the empirical Bayes estimates.