Ming Ye
Environmental Engineering · 2003–2016
A researcher in the Science corpus: 12 papers; trusts 81 researchers and is trusted by 25 researchers here, as inferred from the citation graph.
Subfields
- Environmental Engineering — 78 citations into it; #441 of 5,479 by trust here, #670 by citations
- Statistics and Probability — 76 citations into it; #7,822 of 12,932 by trust here, #6,853 by citations
- Water Science and Technology — 75 citations into it; #546 of 1,711 by trust here, #649 by citations
- Global and Planetary Change — 51 citations into it
- Ocean Engineering — 37 citations into it; #305 of 3,193 by trust here, #540 by citations
- Civil and Structural Engineering — 30 citations into it; #3,384 of 6,160 by trust here, #4,288 by citations
- Artificial Intelligence — 24 citations into it
- Computational Theory and Mathematics — 20 citations into it
Whom Ming Ye trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Shlomo P. Neuman
- Denise Draper
- Jasper A. Vrugt
- Hirotugu Akaike
- Keith Beven
- Jennifer A. Hoeting
- James O. Berger
- P. Meyer
- Hoshin V. Gupta
- Saman Razavi
Who trusts Ming Ye overall
- H. Deng
- H. Dai
- Frank T.-C. Tsai
- Shlomo P. Neuman
- Hoshin V. Gupta
- N. Bulygina
- P. Meyer
- Guannan Zhang
- Weihua Li
- P. Mugunthan
Papers from Ming Ye's perspective
Ordered by how strongly the researchers Ming Ye trusts endorse each paper. Same corpus, a different order for every researcher.
- The future of distributed models: model calibration and uncertainty prediction.
- Effective and efficient global optimization for conceptual rainfall‐runoff models
- Equifinality, data assimilation, and uncertainty estimation in mechanistic modelling of complex environmental systems using the GLUE methodology
- Accelerating Markov Chain Monte Carlo Simulation by Differential Evolution with Self-Adaptive Randomized Subspace Sampling
- Inference from Iterative Simulation Using Multiple Sequences
- Maximum likelihood Bayesian averaging of uncertain model predictions
- Towards a Bayesian total error analysis of conceptual rainfall-runoff models: Characterising model error using storm-dependent parameters
- A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non‐Gaussian errors
- A manifesto for the equifinality thesis
- An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction
Their papers
- An adaptive sparse‐grid high‐order stochastic collocation method for Bayesian inference in groundwater reactive transport modeling (2013) — 97 citations
- Nonlocal and localized analyses of conditional mean transient flow in bounded, randomly heterogeneous porous media (2004) — 84 citations
- Evaluating marginal likelihood with thermodynamic integration method and comparison with several other numerical methods (2016) — 52 citations
- Variance-based global sensitivity analysis for multiple scenarios and models with implementation using sparse grid collocation (2015) — 69 citations
- Global sensitivity analysis in hydrological modeling: Review of concepts, methods, theoretical framework, and applications (2015) — 562 citations
- Assessment of parametric uncertainty for groundwater reactive transport modeling (2014) — 63 citations
- Analysis of regression confidence intervals and Bayesian credible intervals for uncertainty quantification (2012) — 81 citations
- Towards a comprehensive assessment of model structural adequacy (2012) — 435 citations
- Quantification of uncertainty in pedotransfer function‐based parameter estimation for unsaturated flow modeling (2009) — 48 citations
- On model selection criteria in multimodel analysis (2007) — 269 citations
- Sensitivity analysis and assessment of prior model probabilities in MLBMA with application to unsaturated fractured tuff (2005) — 102 citations
- Maximum likelihood Bayesian averaging of spatial variability models in unsaturated fractured tuff (2003) — 232 citations