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of 48
pro vyhledávání: '"Tan, Aixin"'
Autor:
Jin, Rui, Tan, Aixin
Many tools are available to bound the convergence rate of Markov chains in total variation (TV) distance. Such results can be used to establish central limit theorems (CLT) that enable error evaluations of Monte Carlo estimates in practice. However,
Externí odkaz:
http://arxiv.org/abs/2002.09427
Autor:
Jin, Rui, Tan, Aixin
In the past decade, many Bayesian shrinkage models have been developed for linear regression problems where the number of covariates, $p$, is large. Computing the intractable posterior are often done with three-block Gibbs samplers (3BG), based on re
Externí odkaz:
http://arxiv.org/abs/1903.06964
Autor:
Im, Yunju, Tan, Aixin
Publikováno v:
In Computational Statistics and Data Analysis October 2021 162
The naive importance sampling estimator, based on samples from a single importance density, can be numerically unstable. Instead, we consider generalized importance sampling estimators where samples from more than one probability distribution are com
Externí odkaz:
http://arxiv.org/abs/1509.06310
Autor:
Tan, Aixin.
Publikováno v:
RESTRICTED TO UF STUDENTS, STAFF, FACULTY AND ON-CAMPUS USE UNTIL 2011-08-31
Thesis (Ph. D.)--University of Florida, 2009.
Title from title page of source document. Document formatted into pages; contains 77 pages. Includes vita. Includes bibliographical references.
Title from title page of source document. Document formatted into pages; contains 77 pages. Includes vita. Includes bibliographical references.
Externí odkaz:
http://purl.fcla.edu/fcla/etd/UFE0024910
A Markov chain is geometrically ergodic if it converges to its in- variant distribution at a geometric rate in total variation norm. We study geo- metric ergodicity of deterministic and random scan versions of the two-variable Gibbs sampler. We give
Externí odkaz:
http://arxiv.org/abs/1206.4770
Publikováno v:
Statistica Sinica, 2018 Apr 01. 28(2), 1079-1101.
Externí odkaz:
https://www.jstor.org/stable/44841938
Publikováno v:
Bernoulli 2007, Vol. 13, No. 3, 641-652
Consider a parametric statistical model $P(\mathrm{d}x|\theta)$ and an improper prior distribution $\nu(\mathrm{d}\theta)$ that together yield a (proper) formal posterior distribution $Q(\mathrm{d}\theta|x)$. The prior is called strongly admissible i
Externí odkaz:
http://arxiv.org/abs/0709.0448
Autor:
TAN, Aixin, HUANG, Jian
Publikováno v:
The Canadian Journal of Statistics / La Revue Canadienne de Statistique, 2016 Jun 01. 44(2), 180-197.
Externí odkaz:
https://www.jstor.org/stable/44709161
Publikováno v:
Journal of Computational and Graphical Statistics, 2015 Sep 01. 24(3), 792-826.
Externí odkaz:
https://www.jstor.org/stable/24737295