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Relevant bibliographies by topics / Two-parameter Poisson-Dirichlet process

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Academic literature on the topic 'Two-parameter Poisson-Dirichlet process'

Author: Grafiati

Published: 4 June 2021

Last updated: 16 February 2022

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Journal articles on the topic "Two-parameter Poisson-Dirichlet process"

1

Handa, Kenji. "The two-parameter Poisson–Dirichlet point process." Bernoulli 15, no. 4 (2009): 1082–116. http://dx.doi.org/10.3150/08-bej180.

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2

Feng, Shui, and Wei Sun. "Some diffusion processes associated with two parameter Poisson–Dirichlet distribution and Dirichlet process." Probability Theory and Related Fields 148, no. 3-4 (2009): 501–25. http://dx.doi.org/10.1007/s00440-009-0238-2.

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3

Keeler, Holger Paul, and Bartlomiej Blaszczyszyn. "SINR in Wireless Networks and the Two-Parameter Poisson-Dirichlet Process." IEEE Wireless Communications Letters 3, no. 5 (2014): 525–28. http://dx.doi.org/10.1109/lwc.2014.2345691.

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Ruggiero, Matteo. "Species Dynamics in the Two-Parameter Poisson-Dirichlet Diffusion Model." Journal of Applied Probability 51, no. 01 (2014): 174–90. http://dx.doi.org/10.1017/s0021900200010160.

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The recently introduced two-parameter infinitely-many-neutral-alleles model extends the celebrated one-parameter version (which is related to Kingman's distribution) to diffusive two-parameter Poisson-Dirichlet frequencies. In this paper we investigate the dynamics driving the species heterogeneity underlying the two-parameter model. First we show that a suitable normalization of the number of species is driven by a critical continuous-state branching process with immigration. Secondly, we provide a finite-dimensional construction of the two-parameter model, obtained by means of a sequence of

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Ruggiero, Matteo. "Species Dynamics in the Two-Parameter Poisson-Dirichlet Diffusion Model." Journal of Applied Probability 51, no. 1 (2014): 174–90. http://dx.doi.org/10.1239/jap/1395771422.

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The recently introduced two-parameter infinitely-many-neutral-alleles model extends the celebrated one-parameter version (which is related to Kingman's distribution) to diffusive two-parameter Poisson-Dirichlet frequencies. In this paper we investigate the dynamics driving the species heterogeneity underlying the two-parameter model. First we show that a suitable normalization of the number of species is driven by a critical continuous-state branching process with immigration. Secondly, we provide a finite-dimensional construction of the two-parameter model, obtained by means of a sequence of

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6

Du, Lan, Wray Buntine, and Huidong Jin. "A segmented topic model based on the two-parameter Poisson-Dirichlet process." Machine Learning 81, no. 1 (2010): 5–19. http://dx.doi.org/10.1007/s10994-010-5197-4.

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BERTOIN, JEAN. "Two-Parameter Poisson–Dirichlet Measures and Reversible Exchangeable Fragmentation–Coalescence Processes." Combinatorics, Probability and Computing 17, no. 3 (2008): 329–37. http://dx.doi.org/10.1017/s0963548307008784.

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We show that for 0<α<1 and θ>−α, the Poisson–Dirichlet distribution with parameter (α, θ) is the unique reversible distribution of a rather natural fragmentation–coalescence process. This completes earlier results in the literature for certain split-and-merge transformations and the parameter α = 0.

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Feng, Shui, Fuqing Gao, and Youzhou Zhou. "Limit theorems associated with the Pitman–Yor process." Advances in Applied Probability 49, no. 2 (2017): 581–602. http://dx.doi.org/10.1017/apr.2017.13.

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Abstract The Pitman–Yor process is a random discrete measure. The random weights or masses follow the two-parameter Poisson–Dirichlet distribution with parameters 0 < α < 1, θ > -α. The parameters α and θ correspond to the stable and gamma components, respectively. The distribution of atoms is given by a probability η. In this paper we consider the limit theorems for the Pitman–Yor process and the two-parameter Poisson–Dirichlet distribution. These include the law of large numbers, fluctuations, and moderate or large deviation principles. The limiting procedures involve either α tendi

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Ipsen, Yuguang, Ross A. Maller, and Soudabeh Shemehsavar. "A generalised Dickman distribution and the number of species in a negative binomial process model." Advances in Applied Probability 53, no. 2 (2021): 370–99. http://dx.doi.org/10.1017/apr.2020.61.

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AbstractWe derive the large-sample distribution of the number of species in a version of Kingman’s Poisson–Dirichlet model constructed from an $\alpha$ -stable subordinator but with an underlying negative binomial process instead of a Poisson process. Thus it depends on parameters $\alpha\in (0,1)$ from the subordinator and $r>0$ from the negative binomial process. The large-sample distribution of the number of species is derived as sample size $n\to\infty$ . An important component in the derivation is the introduction of a two-parameter version of the Dickman distribution, generalising the

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Labadi, Luai Al, and Mahmoud Zarepour. "On simulations from the two-parameter Poisson-Dirichlet process and the normalized inverse-Gaussian process." Sankhya A 76, no. 1 (2013): 158–76. http://dx.doi.org/10.1007/s13171-013-0033-0.

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Dissertations / Theses on the topic "Two-parameter Poisson-Dirichlet process"

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Al, Labadi Luai. "On New Constructive Tools in Bayesian Nonparametric Inference." Thèse, Université d'Ottawa / University of Ottawa, 2012. http://hdl.handle.net/10393/22917.

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The Bayesian nonparametric inference requires the construction of priors on infinite dimensional spaces such as the space of cumulative distribution functions and the space of cumulative hazard functions. Well-known priors on the space of cumulative distribution functions are the Dirichlet process, the two-parameter Poisson-Dirichlet process and the beta-Stacy process. On the other hand, the beta process is a popular prior on the space of cumulative hazard functions. This thesis is divided into three parts. In the first part, we tackle the problem of sampling from the above mentioned processes

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Book chapters on the topic "Two-parameter Poisson-Dirichlet process"

1

James, Lancelot F. "Large sample asymptotics for the two-parameter Poisson–Dirichlet process." In Institute of Mathematical Statistics Collections. Institute of Mathematical Statistics, 2008. http://dx.doi.org/10.1214/074921708000000147.

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