Parametric Bayesian inference for Y-linked two-sex branching models

Abstract

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A Y-linked two-sex branching process with blind choice is a suitable model for analyzing the evolution of the number of carriers of two alleles of a Y-linked gene in a two-sex monogamous population where each female chooses her partner from among the male population without caring about his type (i.e., the allele he carries). This work focuses on the development of Bayesian inference for this model, considering a parametric framework with the reproduction laws belonging to the power series family of distributions. A sample is considered given by the observation of the total number of females and males (regardless of their types) up to some generation as well as the number of each type of male in the last generation. Using a simulation method based on the Gibbs sampler, we approximate the posterior distributions of the main parameters of this model. The accuracy of the procedure based on this sample is illustrated by way of a simulated example.