Simulation algorithms for integrals of a class of sampling distributions arising in population genetics
Efficient stochastic algorithms are presented in order to simulate allele configurations distributed according to a family pi(A), 0   0, the distribution pi(A) is induced from neutral sampling, whereas for A --> infinity, 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.
independent sampling chinese restaurant process markov chain monte carlo ewens sampling distribution neutrality tests neutral population genetics numerical integration