| Abstract: | Efficient stochastic algorithms are presented in order to simulate allele configurations distributed according to a family π A , 0<A<∞, of exchangeable sampling distributions arising in population genetics. Each distribution π A has two parameters n and k, the sample size and the number of alleles, respectively. For A→0, the distribution π A is induced from neutral sampling, whereas for A→∞, it is induced from Maxwell–Boltzmann sampling. Three different Monte Carlo methods (independent sampling procedures) are provided, based on conditioning, sequential methods and a generalization of Pitmans ‘Chinese restaurant process’. Moreover, an efficient Markov chain Monte Carlo method is provided. The algorithms are applied to the homozygosity test and to the Ewens–Watterson–Slatkin test in order to test the hypothesis of selective neutrality. |
