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arXiv 2102.08520math.PR
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双参数 Poisson-Dirichlet 扩散中的对偶过程

Dual process in the two-parameter Poisson-Dirichlet diffusion

Robert C. Griffiths, Matteo Ruggiero, Dario Spanò, Youzhou Zhou

AI总结:

本文为双参数 Poisson-Dirichlet 扩散识别了对偶过程并求得转移概率,利用带突变的 Kingman 凝结和 Polya 罐方案扩展,给出了扩散转移密度的全新概率证明。

摘要:

双参数 Poisson-Dirichlet 扩散取值于无穷有序单纯形,并扩展了著名的无穷中性等位基因模型,具有双参数 Poisson-Dirichlet 平稳分布。在此,我们为该扩散识别了一个对偶过程,并求得其转移概率。结果表明,该对偶过程由带突变的 Kingman 凝结给出,条件是给定了叶子的配置。有趣的是,该对偶过程仅通过测试函数依赖于平稳分布的附加参数,而不通过转移速率。在讨论了以另一个划分为条件抽取的双参数 Poisson-Dirichlet 划分的抽样概率后,我们利用这些概念连同对偶过程推导出该扩散的转移密度。我们的推导为该结果提供了一种新的概率证明,利用了 Pitman 的 Polya 罐方案的扩展,即在有限步之后将罐分割,随后独立运行两个罐。该证明策略展示了对偶性的强大威力,并可被推广到其他存在对偶过程的模型中。

英文摘要:

The two-parameter Poisson-Dirichlet diffusion takes values in the infinite ordered simplex and extends the celebrated infinitely-many-neutral-alleles model, having a two-parameter Poisson-Dirichlet stationary distribution. Here we identify a dual process for this diffusion and obtain its transition probabilities. The dual is shown to be given by Kingman's coalescent with mutation, conditional on a given configuration of leaves. Interestingly, the dual depends on the additional parameter of the stationary distribution only through the test functions and not through the transition rates. After discussing the sampling probabilities of a two-parameter Poisson-Dirichlet partition drawn conditionally on another partition, we use these notions together with the dual process to derive the transition density of the diffusion. Our derivation provides a new probabilistic proof of this result, leveraging on an extension of Pitman's Polya urn scheme, whereby the urn is split after a finite number of steps and two urns are run independently onwards. The proof strategy exemplifies the power of duality and could be exported to other models where a dual is available.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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