Dual process in the two-parameter Poisson–Dirichlet diffusion

IF 1.1 2区 数学 Q3 STATISTICS & PROBABILITY Stochastic Processes and their Applications Pub Date : 2024-10-05 DOI:10.1016/j.spa.2024.104500
Robert C. Griffiths , Matteo Ruggiero , Dario Spanò , Youzhou Zhou
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Abstract

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 Pólya 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.
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双参数泊松-狄利克特扩散中的双重过程
双参数泊松-狄利克特扩散在无限有序单纯形中取值,并扩展了著名的无限多中性等位基因模型,具有双参数泊松-狄利克特静态分布。在此,我们确定了这种扩散的对偶过程,并获得了其过渡概率。结果表明,对偶过程是以给定的叶子配置为条件,由带有突变的金曼聚合过程给出的。有趣的是,对偶过程只通过检验函数而不是转换率来依赖于静态分布的附加参数。在讨论了以另一个分区为条件得出的双参数泊松-德里克利特分区的采样概率后,我们利用这些概念和对偶过程推导出了扩散的过渡密度。我们的推导为这一结果提供了一个新的概率证明,它利用了皮特曼的波利亚瓮计划的扩展,即在有限步数后拆分瓮,然后两个瓮独立运行。该证明策略体现了对偶性的威力,可用于其他有对偶性的模型。
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来源期刊
Stochastic Processes and their Applications
Stochastic Processes and their Applications 数学-统计学与概率论
CiteScore
2.90
自引率
7.10%
发文量
180
审稿时长
23.6 weeks
期刊介绍: Stochastic Processes and their Applications publishes papers on the theory and applications of stochastic processes. It is concerned with concepts and techniques, and is oriented towards a broad spectrum of mathematical, scientific and engineering interests. Characterization, structural properties, inference and control of stochastic processes are covered. The journal is exacting and scholarly in its standards. Every effort is made to promote innovation, vitality, and communication between disciplines. All papers are refereed.
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