F. Liang

发表

Advanced Markov Chain Monte Carlo Methods: Learning from Past Samples

R. Carroll, Chuanhai Liu, F. Liang, 2010 .

The Multiple-Try Method and Local Optimization in Metropolis Sampling

Jun S. Liu, W. Wong, F. Liang, 2000 .

Fast hybrid Bayesian integrative learning of multiple gene regulatory networks for type 1 diabetes.

Faming Liang, Bochao Jia, F. Liang, 2019, Biostatistics.

A Generalized Wang–Landau Algorithm for Monte Carlo Computation

F. Liang, 2005 .

Real-Parameter Evolutionary Monte Carlo With Applications to Bayesian Mixture Models

W. Wong, F. Liang, 2001 .

Dynamically Weighted Importance Sampling in Monte Carlo Computation

F. Liang, 2002 .

Determination of normalizing constants for simulated tempering

F. Liang, 2005 .

Evolutionary Monte Carlo for protein folding simulations

W. Wong, F. Liang, 2001 .

Comprehensive Computational Pathological Image Analysis Predicts Lung Cancer Prognosis

Junzhou Huang, F. Liang, I. Wistuba, 2017, Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer.

A Theory for Dynamic Weighting in Monte Carlo Computation

Faming Liang, Jun S. Liu, Jun S. Liu, 2001 .

An Imputation-Consistency Algorithm for High-Dimensional Missing Data Problems and Beyond

F. Liang, B. Jia, Qizhai Li, 2018, 1802.02251.

An imputation–regularized optimization algorithm for high dimensional missing data problems and beyond

Faming Liang, Ye Luo, Bochao Jia, 2018, Journal of the Royal Statistical Society. Series B, Statistical methodology.

A double Metropolis–Hastings sampler for spatial models with intractable normalizing constants

Faming Liang, F. Liang, 2010 .

Bayesian site selection for fast Gaussian process regression

Yu Ding, F. Liang, Arash Pourhabib, 2014 .

Average (E)BIC-like Criteria for Bayesian Model Selection

F. Liang, Ye Luo, Jingnan Xue, 2017 .

Bayesian Subset Modeling for High-Dimensional Generalized Linear Models

F. Liang, Kai Yu, Qifan Song, 2013 .

A split‐and‐merge Bayesian variable selection approach for ultrahigh dimensional regression

F. Liang, Qifan Song, 2015 .

High-Dimensional Variable Selection With Reciprocal L1-Regularization

F. Liang, Qifan Song, 2015 .

CLUSTERING GENE EXPRESSION PROFILES USING MIXTURE MODEL ENSEMBLE AVERAGING APPROACH

F. Liang, 2008 .

Au th or ' s pe rs on al co py Dynamic agglomerative clustering of gene expression profiles

F. Liang, Naisyin Wang, 2007 .

Bayesian Models for Sparse Regression Analysis of High Dimensional Data

S. Richardson, J. Rosenthal, B. Mallick, 2012 .

Intrinsic Regression Models for Medial Representation of Subcortical Structures

J. Lieberman, J. Ibrahim, M. Styner, 2012, Journal of the American Statistical Association.

Drug sensitivity prediction with high-dimensional mixture regression

Faming Liang, Runmin Shi, F. Liang, 2019, PloS one.

Bayesian Analysis of Geostatistical Models With an Auxiliary Lattice

Faming Liang, Jincheol Park, F. Liang, 2012 .

An Adaptive Exchange Algorithm for Sampling From Distributions With Intractable Normalizing Constants

Faming Liang, Jun S. Liu, Qifan Song, 2016 .

A Flexible Bayesian Model for Studying Gene–Environment Interaction

Faming Liang, William Wheeler, Maria Teresa Landi, 2012, PLoS genetics.

Longitudinal functional principal component modelling via Stochastic Approximation Monte Carlo

F. Liang, Lan Zhou, R. Carroll, 2010, The Canadian journal of statistics = Revue canadienne de statistique.

Some connections between Bayesian and non-Bayesian methods for regression model selection

F. Liang, 2002 .

Automatic Bayesian model averaging for linear regression and applications in Bayesian curve fitting

Faming Liang, Young K. Truong, Wing Hung Wong, 2001 .

Monte Carlo dynamically weighted importance sampling for spatial models with intractable normalizing constants

Faming Liang, Sooyoung Cheon, F. Liang, 2009 .

A Bayesian Generalized Linear Model for Crimean–Congo Hemorrhagic Fever Incidents

Faming Liang, Nader Ebrahimi, Duchwan Ryu, 2018 .

Stochastic Approximation in Monte Carlo Computation

R. Carroll, Chuanhai Liu, F. Liang, 2007 .

Bayesian Detection of Causal Rare Variants under Posterior Consistency

M. Xiong, F. Liang, 2013, PloS one.

SUPPLEMENTARY MATERIAL FOR "WEAK CONVERGENCE RATES OF POPULATION VERSUS SINGLE-CHAIN STOCHAS- TIC APPROXIMATION MCMC ALGORITHMS "

F. Liang, Qifan Song, Mingqi Wu, 2013 .

Convergence of stochastic approximation algorithms under irregular conditions

F. Liang, Jian Zhang, 2008 .

Extended Stochastic Gradient MCMC for Large-Scale Bayesian Variable Selection

F. Liang, Qifan Song, Yan Sun, 2020, Biometrika.

Statistical and Computational Inverse Problems:Statistical and Computational Inverse Problems

Faming Liang, F. Liang, Jian-Hua Huang, 2006 .

An adaptively weighted stochastic gradient MCMC algorithm for Monte Carlo simulation and global optimization

Guang Lin, F. Liang, Wei Deng, 2022, Statistics and Computing.

A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions

F. Liang, Guang Lin, Wei Deng, 2020, NeurIPS.

Serum Trypsinogen Levels in Type 1 Diabetes

F. Liang, J. Shuster, M. Atkinson, 2017, Diabetes Care.

Joint Bayesian-Incorporating Estimation of Multiple Gaussian Graphical Models to Study Brain Connectivity Development in Adolescence

Faming Liang, Wenxing Hu, Yu-Ping Wang, 2020, IEEE Transactions on Medical Imaging.

Learning Multiple Gene Regulatory Networks in Type 1 Diabetes through a Fast Bayesian Integrative Method

F. Liang, B. Jia, 2018 .

7 D ec 2 01 8 Fast Bayesian Integrative Learning of Multiple Gene Regulatory Networks for Type 1 Diabetes

F. Liang, B. Jia, 2018 .

Fast Bayesian Integrative Learning of Multiple Gene Regulatory Networks for Type 1 Diabetes

F. Liang, B. Jia, T. S. Group, 2018, 1805.02620.

Nonlinear Sufficient Dimension Reduction with a Stochastic Neural Network

F. Liang, Siqi Liang, Yan Sun, 2022, 2210.04349.

STAT5A and STAT5B have opposite correlations with drug response gene expression.

F. Liang, V. Lamba, B. Jia, 2016, Biochemical and biophysical research communications.

BCL-2 (-938C > A) polymorphism is associated with breast cancer susceptibility

F. Liang, Ning Zhang, Xiaoyan Li, 2011, BMC Medical Genetics.

Dynamic Weighting In Markov Chain Monte Carlo

Faming Liang, Jun S. Liu, Jun S. Liu, 2007 .

Aberrant Brain Connectivity in Schizophrenia Detected via a Fast Gaussian Graphical Model

Faming Liang, Yuping Wang, Jian Fang, 2019, IEEE Journal of Biomedical and Health Informatics.

Non-reversible Parallel Tempering for Deep Posterior Approximation

F. Liang, Qian Zhang, Wei Deng, 2022, AAAI.

Dynamic weighting in simulations of spin systems

W. Wong, F. Liang, 1999 .

Markov neighborhood regression for statistical inference of high‐dimensional generalized linear models

F. Liang, Lizhe Sun, 2022, Statistics in medicine.

A Double Regression Method for Graphical Modeling of High-dimensional Nonlinear and Non-Gaussian Data

F. Liang, Siqi Liang, 2022, 2212.04585.

TRAJECTORY AVERAGING FOR STOCHASTIC APPROXIMATION MCMC ALGORITHMS 1

F. Liang, 2010 .

Parallel and interacting stochastic approximation annealing algorithms for global optimisation

F. Liang, G. Karagiannis, B. Konomi, 2016, Statistics and Computing.

On the use of stochastic approximation Monte Carlo for Monte Carlo integration

F. Liang, 2009 .

Double-Parallel Monte Carlo for Bayesian analysis of big data

F. Liang, Jingnan Xue, 2017, Statistics and Computing.

Stochastic approximation Monte Carlo importance sampling for approximating exact conditional probabilities

Yuguo Chen, F. Liang, Kai Yu, 2013, Statistics and Computing.

Bayesian Neural Networks for Selection of Drug Sensitive Genes

F. Liang, Qizhai Li, Lei Zhou, 2018, Journal of the American Statistical Association.

Statistical and Computational Inverse Problems

Faming Liang, Jianhua Huang, F. Liang, 2006, Technometrics.

Learning Gene Regulatory Networks with High-Dimensional Heterogeneous Data

Faming Liang, Bochao Jia, F. Liang, 2018, 1805.02547.

Dynamic agglomerative clustering of gene expression profiles

Faming Liang, Naisyin Wang, F. Liang, 2007, Pattern Recognit. Lett..

Markov Neighborhood Regression for High-Dimensional Inference

Faming Liang, Bochao Jia, Jingnan Xue, 2020, Journal of the American Statistical Association.

A two-stage approach of gene network analysis for high-dimensional heterogeneous data.

Faming Liang, Ling Cai, Guanghua Xiao, 2018, Biostatistics.

An Equivalent Measure of Partial Correlation Coefficients for High-Dimensional Gaussian Graphical Models

Faming Liang, Peihua Qiu, Qifan Song, 2015 .

An Effective Bayesian Neural Network Classifier with a Comparison Study to Support Vector Machine

Faming Liang, F. Liang, 2003, Neural Computation.

Nonlinear Variable Selection via Deep Neural Networks

Xiao Wang, Yao Chen, Qingyi Gao, 2020, J. Comput. Graph. Stat..

Author ' s personal copy Bayesian phylogeny analysis via stochastic approximation Monte Carlo

F. Liang, Sooyoung Cheon, 2009 .

Phylogenetic tree construction using sequential stochastic approximation Monte Carlo

Faming Liang, Sooyoung Cheon, F. Liang, 2008, Biosyst..

Search for haplotype interactions that influence susceptibility to type 1 diabetes, through use of unphased genotype data.

W. Dassen, F. Liang, M. D. de Gunst, 2003, American journal of human genetics.

Bayesian Modeling of ChIP‐chip Data Through a High‐Order Ising Model

Faming Liang, Qianxing Mo, F. Liang, 2010, Biometrics.

A hidden Ising model for ChIP-chip data analysis

Faming Liang, Qianxing Mo, F. Liang, 2010, Bioinform..

Significantly mutated genes and regulatory pathways in SCLC-a meta-analysis.

F. Liang, J. Yokota, T. Kohno, 2017, Cancer genetics.

Markov Chain Monte Carlo: Innovations And Applications

Faming Liang, Wilfrid S. Kendall, W. Kendall, 2006 .

Enhanced construction of gene regulatory networks using hub gene information

F. Liang, Donghyeon Yu, Johan Lim, 2017, BMC Bioinformatics.

A Robust Model-Free Feature Screening Method for Ultrahigh-Dimensional Data

Faming Liang, Jingnan Xue, F. Liang, 2017, Journal of computational and graphical statistics : a joint publication of American Statistical Association, Institute of Mathematical Statistics, Interface Foundation of North America.

Nearly optimal Bayesian shrinkage for high-dimensional regression

F. Liang, Qifan Song, 2017, Science China Mathematics.

A BAYESIAN MARK INTERACTION MODEL FOR ANALYSIS OF TUMOR PATHOLOGY IMAGES.

Faming Liang, Guanghua Xiao, Qiwei Li, 2018, The annals of applied statistics.

A Bayesian hidden Potts mixture model for analyzing lung cancer pathology images.

Faliu Yi, Faming Liang, Yang Xie, 2019, Biostatistics.

Author's Personal Copy Computational Statistics and Data Analysis Learning Bayesian Networks for Discrete Data

Faming Liang, Jian Zhang, F. Liang, 2022 .

Estimating the false discovery rate using the stochastic approximation algorithm

F. Liang, Jian Zhang, Faming Liang, 2008 .

A ROBUST SEQUENTIAL BAYESIAN METHOD FOR IDENTIFICATION OF DIFFERENTIALLY EXPRESSED GENES

Chuanhai Liu, F. Liang, Naisyin Wang, 2007 .

A Bayesian Generalized Linear Model for Crimean–Congo Hemorrhagic Fever Incidents

F. Liang, Ö. Ergönül, D. Ryu, 2017, Journal of Agricultural, Biological and Environmental Statistics.

Biomarker detection and categorization in ribonucleic acid sequencing meta‐analysis using Bayesian hierarchical models

Faming Liang, Tianzhou Ma, George Tseng, 2017, Journal of the Royal Statistical Society. Series C, Applied statistics.

Improving SAMC using smoothing methods: Theory and applications to Bayesian model selection problems

Faming Liang, F. Liang, 2009, 0908.3553.

Continuous Contour Monte Carlo for Marginal Density Estimation With an Application to a Spatial Statistical Model

F. Liang, 2007 .

A Joint Bayesian Model for Integrating Microarray and RNA Sequencing Transcriptomic Data

Faming Liang, George C. Tseng, Tianzhou Ma, 2017, J. Comput. Biol..

Annealing evolutionary stochastic approximation Monte Carlo for global optimization

Faming Liang, F. Liang, 2011, Stat. Comput..

Simulated Stochastic Approximation Annealing for Global Optimization With a Square-Root Cooling Schedule

Faming Liang, Yichen Cheng, Guang Lin, 2014 .

Weak Convergence Rates of Population Versus Single-Chain Stochastic Approximation MCMC Algorithms

F. Liang, Qifan Song, Mingqi Wu, 2013, Advances in Applied Probability.

Comment: “Modeling an Augmented Lagrangian for Blackbox Constrained Optimization” by Gramacy et al.

Faming Liang, Yichen Cheng, F. Liang, 2016, Technometrics.

Learning gene regulatory networks from next generation sequencing data

Faming Liang, Guanghua Xiao, Bochao Jia, 2017, Biometrics.

Inflammatory Gene Haplotype-Interaction Networks Involved in Coronary Collateral Formation

F. Liang, D. Grobbee, Y. van der Graaf, 2008, Human Heredity.

An Adaptive Empirical Bayesian Method for Sparse Deep Learning

Wei Deng, Faming Liang, Guang Lin, 2019, NeurIPS.

A Monte Carlo Metropolis-Hastings Algorithm for Sampling from Distributions with Intractable Normalizing Constants

Faming Liang, Ick-Hoon Jin, F. Liang, 2013, Neural Computation.

Consistent Sparse Deep Learning: Theory and Computation

F. Liang, Qifan Song, Yan Sun, 2021, Journal of the American Statistical Association.

A Resampling-Based Stochastic Approximation Method for Analysis of Large Geostatistical Data

F. Liang, Jincheol Park, P. Yang, 2013 .

Bayesian neural networks for nonlinear time series forecasting

Faming Liang, F. Liang, 2005, Stat. Comput..

A Split-and-Merge Approach for Singular Value Decomposition of Large-Scale Matrices.

F. Liang, Q. Mo, Runmin Shi, 2016, Statistics and its Interface.

Use of SVD-based probit transformation in clustering gene expression profiles

Faming Liang, F. Liang, 2007, Comput. Stat. Data Anal..

A kernel‐expanded stochastic neural network pdf

Faming Liang, Yan Sun, F. Liang, 2022, Journal of the Royal Statistical Society: Series B (Statistical Methodology).

Annealing stochastic approximation Monte Carlo algorithm for neural network training

Faming Liang, F. Liang, 2007, Machine Learning.

Supplementary Material for “Bayesian Neural Networks for Selection of Anticancer Drug Response Genes”

F. Liang, Qizhai Li, Lei Zhou, 2017 .

Stochastic gradient Langevin dynamics with adaptive drifts

Faming Liang, Sehwan Kim, Qifan Song, 2021, Journal of statistical computation and simulation.

Stochastic approximation Hamiltonian Monte Carlo

F. Liang, Minsuk Shin, Jonghyun Yun, 2018, 1810.04811.

Adaptive evolutionary Monte Carlo algorithm for optimization with applications to sensor placement problems

Faming Liang, Yu Ding, Yuan Ren, 2008, Stat. Comput..

Parallel and interacting stochastic approximation annealing algorithms for global optimisation

Faming Liang, Guang Lin, Georgios Karagiannis, 2015, Stat. Comput..

Estimating uncertainty of streamflow simulation using Bayesian neural networks

Faming Liang, Xuesong Zhang, Raghavan Srinivasan, 2009 .

Stochastic approximation Monte Carlo importance sampling for approximating exact conditional probabilities

Faming Liang, Yuguo Chen, Kai Yu, 2014, Stat. Comput..

Stochastic Gradient Langevin Dynamics Algorithms with Adaptive Drifts pdf

Sehwan Kim, Faming Liang, Qifan Song, 2020, ArXiv.

Sea Surface Temperature Modeling using Radial Basis Function Networks With a Dynamically Weighted Particle Filter

Faming Liang, Bani K. Mallick, Duchwan Ryu, 2013 .

EVOLUTIONARY MONTE CARLO: APPLICATIONS TO Cp MODEL SAMPLING AND CHANGE POINT PROBLEM

Faming Liang, Wing Hung Wong, W. Wong, 2000 .

Model Based Clustering with Generally Dependent Data: An Exponential Power Mixture Approach

F. Liang, Jian Zhang, 2007 .

PURE: A Framework for Analyzing Proximity-based Contact Tracing Protocols pdf

Elisa Bertino, Ninghui Li, Yang Yang, 2020, ACM Comput. Surv..

Contour Monte Carlo: Theoretical Results, Practical Considerations, and Applications

Faming Liang, F. Liang, 2004 .

An Overview of Stochastic Approximation Monte Carlo

Faming Liang, F. Liang, 2014 .

Efficient p-value evaluation for resampling-based tests.

Faming Liang, Nilanjan Chatterjee, Kai Yu, 2011, Biostatistics.

Robust Clustering Using Exponential Power Mixtures

F. Liang, Jian Zhang, 2010, Biometrics.

Interacting Contour Stochastic Gradient Langevin Dynamics

Botao Hao, F. Liang, Siqi Liang, 2022, ICLR.

Sufficient Dimension Reduction with Deep Neural Networks for Phenotype Prediction

Faming Liang, Wei-Heng Huang, Siqi Liang, 2021, Proceedings of the 3rd International Conference on Statistics: Theory and Applications.

Stochastic Approximation Algorithms for Estimation of Spatial Mixed Models

B. Peterson, F. Liang, M. Gu, 2007 .

Bayesian Analysis for Exponential Random Graph Models Using the Adaptive Exchange Sampler.

Faming Liang, Ying Yuan, Ick Hoon Jin, 2013, Statistics and its interface.

Fitting Social Network Models Using Varying Truncation Stochastic Approximation MCMC Algorithm

F. Liang, Ick Hoon Jin, 2013 .

Bayesian Analysis for Exponential Random Graph Models Using the Double Metropolis-Hastings Sampler

Faming Liang, Ick Hoon Jin, F. Liang, 2009 .

Explicitly integrating parameter, input, and structure uncertainties into Bayesian Neural Networks for probabilistic hydrologic forecasting

Faming Liang, Ziliang Zong, Xuesong Zhang, 2011 .

Crash Injury Severity Analysis Using a Bayesian Ordered Probit Model

Faming Liang, Yuanchang Xie, Yunlong Zhang, 2007 .

A fast multilocus test with adaptive SNP selection for large-scale genetic-association studies

Faming Liang, William Wheeler, Jianxin Shi, 2013, European Journal of Human Genetics.

Annealing contour Monte Carlo algorithm for structure optimization in an off-lattice protein model.

Faming Liang, F. Liang, 2004, The Journal of chemical physics.

Lung Cancer Pathological Image Analysis Using a Hidden Potts Model

Faliu Yi, Faming Liang, Guanghua Xiao, 2017, Cancer informatics.

Bayesian Peak Picking for NMR Spectra

Faming Liang, Xin Gao, Yichen Cheng, 2013, Genom. Proteom. Bioinform..

Trajectory averaging for stochastic approximation MCMC algorithms

F. Liang, 2010, 1011.2587.

Learning Sparse Deep Neural Networks with a Spike-and-Slab Prior.

F. Liang, Qifan Song, Y. Sun, 2022, Statistics & probability letters.

Bayesian modeling of ChIP-chip data using latent variables

Faming Liang, Mingqi Wu, Yanan Tian, 2009, BMC Bioinformatics.

Integrative Analysis of Gene Networks and Their Application to Lung Adenocarcinoma Studies

Faming Liang, Ling Cai, Guanghua Xiao, 2017, Cancer informatics.

Markov Chain Monte Carlo: Innovations And Applications

W. Kendall, F. Liang, J-S Wang, 2006 .

An Imputation-Consistency Algorithm for High-Dimensional Missing Data Problems and Beyond

F. Liang, B. Jia, Qizhai Li, 2016 .

Joint estimation of multiple mixed graphical models for pan‐cancer network analysis

Faming Liang, Bochao Jia, F. Liang, 2020, Stat.

A Likelihood Gradient Free Algorithm for Fast Training of Restricted Boltzmann Machines

F. Liang, 2017 .

Dynamic weighting Monte Carlo for constrained floorplan designs in mixed signal application

Faming Liang, Jason Cong, Wing Hung Wong, 2000, Proceedings 2000. Design Automation Conference. (IEEE Cat. No.00CH37106).

Dynamic Weighting In Markov Chain Monte

W. Wong, F. Liang, S. Liu, 1998 .

A Blockwise Consistency Method for Parameter Estimation of Complex Models

Faming Liang, Malay Ghosh, Ye Luo, 2018, Sankhya B.

Double-Parallel Monte Carlo for Bayesian analysis of big data

Faming Liang, Jingnan Xue, F. Liang, 2017, Statistics and Computing.

Bayesian Analysis of Exponential Random Graph Models Using Stochastic Gradient Markov Chain Monte Carlo

F. Liang, Qian Zhang, 2023, Bayesian Analysis.

Extended Stochastic Gradient MCMC for Large-Scale Bayesian Variable Selection pdf

Faming Liang, Mao Ye, Yan Sun, 2020, Biometrika.

Use of SAMC for Bayesian analysis of statistical models with intractable normalizing constants

Faming Liang, Ick-Hoon Jin, F. Liang, 2014, Comput. Stat. Data Anal..

A New Paradigm for Generative Adversarial Networks based on Randomized Decision Rules

F. Liang, Qifan Song, Sehwan Kim, 2023, Statistica Sinica.

A BAYESIAN MARK INTERACTION MODEL FOR ANALYSIS OF TUMOR PATHOLOGY IMAGES.

F. Liang, Guanghua Xiao, Xinlei Wang, 2018, The annals of applied statistics.

Modeling the Relationship Between EDI Implementation and Firm Performance Improvement With Neural Networks

Faming Liang, Guoqiang Peter Zhang, Craig A. Hill, 2010, IEEE Transactions on Automation Science and Engineering.

Enhanced construction of gene regulatory networks using hub gene information

Faming Liang, Guanghua Xiao, Xinlei Wang, 2017, BMC Bioinformatics.

Identification of factors impacting on the transmission and mortality of COVID-19

Faming Liang, Ninghui Li, Peiyi Zhang, 2021, Journal of applied statistics.

The 2016 Technometrics Prizes

Faming Liang, Jinsu Kim, F. Liang, 2017, Technometrics.

Relaxed simulated tempering for VLSI floorplan designs

Faming Liang, Jason Cong, Wing Hung Wong, 1999, Proceedings of the ASP-DAC '99 Asia and South Pacific Design Automation Conference 1999 (Cat. No.99EX198).

Statistical and Computational Inverse Problems

F. Liang, Jianhua Huang, 2006, Technometrics.

Learning Moral Graphs in Construction of High-Dimensional Bayesian Networks for Mixed Data

Faming Liang, Bochao Jia, Suwa Xu, 2019, Neural Computation.

Sparse Deep Learning: A New Framework Immune to Local Traps and Miscalibration pdf

Faming Liang, Yan Sun, Wenjun Xiong, 2021, NeurIPS.

Fitting Social Network Models Using Varying Truncation Stochastic Approximation MCMC Algorithm

F. Liang, Ick Hoon Jin, 2013 .