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pro vyhledávání: '"Wong Wing H"'
Traditional MCMC algorithms are computationally intensive and do not scale well to large data. In particular, the Metropolis-Hastings (MH) algorithm requires passing over the entire dataset to evaluate the likelihood ratio in each iteration. We propo
Externí odkaz:
http://arxiv.org/abs/1908.02910
Autor:
Gennert, David G., Lynn, Rachel C., Granja, Jeff M., Weber, Evan W., Mumbach, Maxwell R., Zhao, Yang, Duren, Zhana, Sotillo, Elena, Greenleaf, William J., Wong, Wing H., Satpathy, Ansuman T., Mackall, Crystal L., Chang, Howard Y.
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2021 Jul . 118(30), 1-12.
Externí odkaz:
https://www.jstor.org/stable/27052636
Autor:
Li, Dangna, Wong, Wing H
In this paper we propose a general framework of performing MCMC with only a mini-batch of data. We show by estimating the Metropolis-Hasting ratio with only a mini-batch of data, one is essentially sampling from the true posterior raised to a known t
Externí odkaz:
http://arxiv.org/abs/1707.09705
In our recent paper, we showed that in exponential family, contrastive divergence (CD) with fixed learning rate will give asymptotically consistent estimates \cite{wu2016convergence}. In this paper, we establish consistency and convergence rate of CD
Externí odkaz:
http://arxiv.org/abs/1605.06220
The Contrastive Divergence (CD) algorithm has achieved notable success in training energy-based models including Restricted Boltzmann Machines and played a key role in the emergence of deep learning. The idea of this algorithm is to approximate the i
Externí odkaz:
http://arxiv.org/abs/1603.05729
Approximate Bayesian Computation (ABC) methods are used to approximate posterior distributions in models with unknown or computationally intractable likelihoods. Both the accuracy and computational efficiency of ABC depend on the choice of summary st
Externí odkaz:
http://arxiv.org/abs/1510.02175
Autor:
Bo Zhou, Purmann, Carolin, Hanmin Guo, GiWon Shin, Yiling Huang, Pattni, Reenal, Qingxi Meng, Greer, Stephanie U., Roychowdhury, Tanmoy, Wood, Raegan N., Ho, Marcus, zu Dohna, Heinrich, Abyzov, Alexej, Hallmayer, Joachim F., Wong, Wing H., Ji, Hanlee P., Urban, Alexander E.
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America; 7/30/2024, Vol. 121 Issue 31, p1-12, 20p
Publikováno v:
The Annals of Statistics, 2018 Dec 01. 46(6A), 3067-3098.
Externí odkaz:
https://www.jstor.org/stable/26542894
Autor:
Tanner, Martin A., Wong, Wing H.
Publikováno v:
Statistical Science 2010, Vol. 25, No. 4, 506-516
It was known from Metropolis et al. [J. Chem. Phys. 21 (1953) 1087--1092] that one can sample from a distribution by performing Monte Carlo simulation from a Markov chain whose equilibrium distribution is equal to the target distribution. However, it
Externí odkaz:
http://arxiv.org/abs/1104.2210
Autor:
Ma, Li, Wong, Wing H.
Testing and characterizing the difference between two data samples is of fundamental interest in statistics. Existing methods such as Kolmogorov-Smirnov and Cramer-von-Mises tests do not scale well as the dimensionality increases and provides no easy
Externí odkaz:
http://arxiv.org/abs/1011.1253