Bayesian analysis of the discovery process model using transdimensional Markov chain Monte Carlo

Based upon the Bayesian framework of analyzing the discovery sequence in a play, a transdiniensional Markov chain Monte Carlo algorithm is employed to sample model parameters and pool sizes from their joint posterior distribution. The proposed transdimensional jump algorithm allows for jumps between spaces of different dimensions. The sampling scheme ensures that all the parameter space of changing dimension can be traversed in spite of the unknown number of pools. The equal sample weights make it easy to obtain the confidence intervals and assess the statistical error in the estimates, so that the statistical behaviors of the discovery process modeling can be well understood.