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| 1 | A 2.833-kb Insertion in BnFLC.A2 and Its Homeologous Exchange with BnFLC.C2 during Breeding Selection Generated Early-Flowering Rapeseed显示文摘 | Lei Chen Faming Dong Jing Cai Qiang Xin Caochuang Fang Liang Liu Lili Wan Guangsheng Yang Dengfeng Hong | 2018 | Molecular Plant2018，11，1： | 6 |
| 2 | Bayesian neural networks for nonlinear time series forecasting 显示文摘 | Liang Faming | 2005 | Statistics and Computing2005，15，： | 1 |
| 3 | Bayesian neural networks for nonlinear time series forecasting显示文摘 | Faming Liang | 2005 | Statistics and Computing2005，15，： | 1 |
| 4 | Real-parameter evolutionary Monte Carlo with applications to Bayesian mixture models显示文摘 | LIANG Faming Wong W H | 2001 | Journal of the American Statistical Association2001，96，454： | 1 |
| 5 | Evolutionary Monte Carlo:Applications to Cp Model Sampling and Change Point Problem显示文摘 | Liang Faming Wong Wing Hung | 2000 | Statistica Sinica2000，10，： | 1 |
| 6 | Evolutionary Monte Carlo:applications to cp model sampling and change point problem显示文摘 | Liang Faming Wong W H | 2000 | Statistica Sinica2000，10，2： | 1 |
| 7 | Real-parameter evolutionary Monte Carlo with applications to bayesian mixture models显示文摘 | Liang Faming Wong W H | 2001 | Journal of the American Statistical Association2001，96，454： | 1 |
| 8 | Adaptive evolutionary Monte Carlo algorithm for optimization with appliciations to sensor placement problems显示文摘 | Ren Yuan Ding Yu Liang Faming | 2008 | Statistics and Computing2008，18，： | 1 |
| 9 | Bayesian neural networks for nonlinear time series forescasting显示文摘 | Liang Faming | 2005 | Statistics and Computing2005，15，： | 1 |
| 10 | Evolutionary Monte Carlo for protein folding simulations显示文摘 | LIANG Faming WONG Winghung | 2001 | Chemical Physics2001，115，7： | 1 |
| 11 | Real-parameter evolutionary Monte Carlo with applications to Bayesian mixture models显示文摘 | Liang Faming Wong W H | 2001 | Journal of the American Statistical Association2001，96，454： | 1 |
| 12 | Nearly optimal Bayesian shrinkage for high-dimensional regression显示文摘During the past decade,shrinkage priors have received much attention in Bayesian analysis of high-dimensional data.This paper establishes the posterior consistency for high-dimensional linear regression with a class of shrinkage priors,which has a heavy and flat tail and allocates a sufficiently large probability mass in a very small neighborhood of zero.While enjoying its efficiency in posterior simulations,the shrinkage prior can lead to a nearly optimal posterior contraction rate and the variable selection consistency as the spike-and-slab prior.Our numerical results show that under the posterior consistency,Bayesian methods can yield much better results in variable selection than the regularization methods such as LASSO and SCAD.This paper also establishes a BvM-type result,which leads to a convenient way of uncertainty quantification for regression coefficient estimates. | Qifan Song Faming Liang | 2023 | Science China Mathematics2023，66，2： | 0 |
| 13 | A Global Spectral Element Model for Poisson Equations and Advective Flow over a Sphere显示文摘A global spherical Fourier–Legendre spectral element method is proposed to solve Poisson equations and advective flow over a sphere. In the meridional direction, Legendre polynomials are used and the region is divided into several elements. In order to avoid coordinate singularities at the north and south poles in the meridional direction, Legendre–Gauss–Radau points are chosen at the elements involving the two poles. Fourier polynomials are applied in the zonal direction for its periodicity,with only one element. Then, the partial differential equations are solved on the longitude–latitude meshes without coordinate transformation between spherical and Cartesian coordinates. For verification of the proposed method, a few Poisson equations and advective flows are tested. Firstly, the method is found to be valid for test cases with smooth solution. The results of the Poisson equations demonstrate that the present method exhibits high accuracy and exponential convergence. Highprecision solutions are also obtained with near negligible numerical diffusion during the time evolution for advective flow with smooth shape. Secondly, the results of advective flow with non-smooth shape and deformational flow are also shown to be reasonable and effective. As a result, the present method is proved to be capable of solving flow through different types of elements, and thereby a desirable method with reliability and high accuracy for solving partial differential equations over a sphere. | Huan MEI Faming WANG Zhong ZENG Zhouhua QIU Linmao YIN Liang LI | 2016 | Advances in Atmospheric Sciences2016，33，3： | 0 |
| 14 | Bayesian Peak Picking for NMR Spectra显示文摘Protein structure determination is a very important topic in structural genomics,which helps people to understand varieties of biological functions such as protein-protein interactions,protein–DNA interactions and so on.Nowadays,nuclear magnetic resonance(NMR) has often been used to determine the three-dimensional structures of protein in vivo.This study aims to automate the peak picking step,the most important and tricky step in NMR structure determination.We propose to model the NMR spectrum by a mixture of bivariate Gaussian densities and use the stochastic approximation Monte Carlo algorithm as the computational tool to solve the problem.Under the Bayesian framework,the peak picking problem is casted as a variable selection problem.The proposed method can automatically distinguish true peaks from false ones without preprocessing the data.To the best of our knowledge,this is the first effort in the literature that tackles the peak picking problem for NMR spectrum data using Bayesian method. | Yichen Cheng Xin Gao Faming Liang | 2014 | Genomics, Proteomics & Bioinformatics2014，12，1： | 0 |
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