| The multiple-try method and local optimization in Metropolis sampling JS Liu, F Liang, WH Wong Journal of the American Statistical Association 95 (449), 121-134, 2000 | 552 | 2000 |
| Advanced Markov chain Monte Carlo methods: learning from past samples F Liang, C Liu, R Carroll John Wiley & Sons, 2011 | 388 | 2011 |
| Stochastic approximation in Monte Carlo computation F Liang, C Liu, RJ Carroll Journal of the American Statistical Association 102 (477), 305-320, 2007 | 303 | 2007 |
| Real-parameter evolutionary Monte Carlo with applications to Bayesian mixture models F Liang, WH Wong Journal of the American Statistical Association 96 (454), 653-666, 2001 | 283 | 2001 |
| Evolutionary Monte Carlo for protein folding simulations F Liang, WH Wong The Journal of Chemical Physics 115 (7), 3374-3380, 2001 | 281 | 2001 |
| Crash injury severity analysis using Bayesian ordered probit models Y Xie, Y Zhang, F Liang Journal of Transportation Engineering 135 (1), 18-25, 2009 | 239 | 2009 |
| EVOLUTIONARY MONTE CARLO: APPLICATIONS TO C p MODEL SAMPLING AND CHANGE POINT PROBLEM F Liang, WH Wong Statistica sinica, 317-342, 2000 | 230 | 2000 |
| A double Metropolis–Hastings sampler for spatial models with intractable normalizing constants F Liang Journal of Statistical Computation and Simulation 80 (9), 1007-1022, 2010 | 199 | 2010 |
| Comprehensive computational pathological image analysis predicts lung cancer prognosis X Luo, X Zang, L Yang, J Huang, F Liang, J Rodriguez-Canales, ... Journal of Thoracic Oncology 12 (3), 501-509, 2017 | 178 | 2017 |
| Bayesian neural networks for nonlinear time series forecasting F Liang Statistics and computing 15, 13-29, 2005 | 129 | 2005 |
| Dynamic weighting in Monte Carlo and optimization WH Wong, F Liang Proceedings of the National Academy of Sciences 94 (26), 14220-14224, 1997 | 124 | 1997 |
| Enhanced construction of gene regulatory networks using hub gene information D Yu, J Lim, X Wang, F Liang, G Xiao BMC bioinformatics 18, 1-20, 2017 | 116 | 2017 |
| Nearly optimal Bayesian shrinkage for high-dimensional regression Q Song, F Liang Science China Mathematics 66 (2), 409-442, 2023 | 103 | 2023 |
| Bayesian neural networks for selection of drug sensitive genes F Liang, Q Li, L Zhou Journal of the American Statistical Association 113 (523), 955-972, 2018 | 98 | 2018 |
| Estimating uncertainty of streamflow simulation using Bayesian neural networks X Zhang, F Liang, R Srinivasan, M Van Liew Water resources research 45 (2), 2009 | 97 | 2009 |
| Bayesian subset modeling for high-dimensional generalized linear models F Liang, Q Song, K Yu Journal of the American Statistical Association 108 (502), 589-606, 2013 | 96 | 2013 |
| A resampling-based stochastic approximation method for analysis of large geostatistical data F Liang, Y Cheng, Q Song, J Park, P Yang Journal of the American Statistical Association 108 (501), 325-339, 2013 | 92 | 2013 |
| A split-and-merge Bayesian variable selection approach for ultrahigh dimensional regression Q Song, F Liang Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2015 | 88 | 2015 |
| A generalized Wang–Landau algorithm for Monte Carlo computation F Liang Journal of the American Statistical Association 100 (472), 1311-1327, 2005 | 76 | 2005 |
| Markov chain Monte Carlo: innovations and applications WS Kendall, F Liang, JS Wang World Scientific, 2005 | 76 | 2005 |
