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Non-parametric Bayesian inference through MCMC method for Y-linked two-sex branching processes with blind choice

机译:基于MCMC方法的非参数贝叶斯盲选择Y链接两性分支过程

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Dirichlet-process-based non-parametric Bayesian inference is developed for a Y-linked two-sex branching process with blind choice. This stochastic model is suitable for analysing 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). The only data assumed to be available are the total number of females and males (regardless of their types) up to some generation and the numbers of each type of male in the last generation. A simulation method which is based on a Dirichlet process and a Gibbs sampler is developed to estimate the posterior distributions of the model's main parameters. Finally, the computational efficiency of the algorithm is illustrated with example simulations and an application to real data.
机译:基于狄利克雷过程的非参数贝叶斯推理是为带有盲目选择的Y链接两性分支过程开发的。该随机模型适用于分析两性一夫一妻制人口中Y连锁基因的两个等位基因携带者数目的演变,其中每个女性从男性人口中选择伴侣而不关心其类型(即等位基因)他携带)。假定唯一可用的数据是直到某一代的雌性和雄性总数(不论其类型如何)以及上一代中每种类型的雄性数量。开发了一种基于Dirichlet过程和Gibbs采样器的模拟方法来估计模型主要参数的后验分布。最后,通过示例仿真和对实际数据的应用说明了该算法的计算效率。

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