Kernel based Dirichlet sequences-Reference-Cited by-同舟云学术

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Kernel based Dirichlet sequences

Published:2023-05-01 Issue:2 Volume:29 Page:
ISSN:1350-7265
Container-title:Bernoulli
language:
Short-container-title:Bernoulli

Author:

Berti Patrizia¹,Dreassi Emanuela²,Leisen Fabrizio³,Pratelli Luca⁴,Rigo Pietro⁵

Affiliation:

1. Dipartimento di Matematica Pura ed Applicata “G. Vitali”, Università di Modena e Reggio-Emilia, via Campi 213/B, 41100 Modena, Italy

2. Dipartimento di Statistica, Informatica, Applicazioni, Università di Firenze, viale Morgagni 59, 50134 Firenze, Italy

3. School of Mathematical Sciences, University of Nottingham, University Park, Nottingham, NG7 2RD, UK

4. Accademia Navale, viale Italia 72, 57100 Livorno, Italy

5. Dipartimento di Scienze Statistiche “P. Fortunati”, Università di Bologna, via delle Belle Arti 41, 40126 Bologna, Italy

Publisher

Bernoulli Society for Mathematical Statistics and Probability

Subject

Statistics and Probability

Reference24 articles.

1. Antoniak, C.E. (1974). Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems. Ann. Statist. 2 1152–1174.

2. Blackwell, D. and MacQueen, J.B. (1973). Ferguson distributions via Pólya urn schemes. Ann. Statist. 1 353–355.

3. Ferguson, T.S. (1973). A Bayesian analysis of some nonparametric problems. Ann. Statist. 1 209–230.

4. Berti, P., Pratelli, L. and Rigo, P. (2004). Limit theorems for a class of identically distributed random variables. Ann. Probab. 32 2029–2052. 10.1214/009117904000000676

5. Ghosal, S. and van der Vaart, A. (2017). Fundamentals of Nonparametric Bayesian Inference. Cambridge Series in Statistical and Probabilistic Mathematics 44. Cambridge: Cambridge Univ. Press. 10.1017/9781139029834

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Characterization of exchangeable measure-valued Pólya urn sequences;Electronic Journal of Probability;2024-01-01

2. Bayesian Predictive Inference Without a Prior;Statistica Sinica;2024

3. A Probabilistic View on Predictive Constructions for Bayesian Learning;Statistical Science;2023-01-01

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