G. Schoups
Global and Planetary Change · 2010
A researcher in the Science corpus: 3 papers; trusts 28 researchers and is trusted by 21 researchers here, as inferred from the citation graph.
Subfields
- Global and Planetary Change — 26 citations into it; #861 of 10,225 by trust here, #4,014 by citations
- Water Science and Technology — 25 citations into it; #76 of 1,711 by trust here, #514 by citations
- Environmental Engineering — 13 citations into it; #4,088 of 5,479 by trust here, #3,797 by citations
- Atmospheric Science — 7 citations into it; #3,460 of 4,804 by trust here, #3,555 by citations
- Ocean Engineering — 6 citations into it; #199 of 3,193 by trust here, #764 by citations
- Statistics and Probability — 6 citations into it
Whom G. Schoups trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Jasper A. Vrugt
- C. T. ter Braak
- Dmitri Kavetski
- George A. Kuczera
- Hoshin V. Gupta
- Branislav @. Nikoli
- Benjamin Renard
- Mark A. Thyer
- B. Schaefli
- Demetris Koutsoyiannis
Who trusts G. Schoups overall
- Jasper A. Vrugt
- Mojtaba Sadegh
- Dan Lu
- Eric Laloy
- Ming Ye
- Steven V. Weijs
- Budiman Minasny
- Guannan Zhang
- Hoshin V. Gupta
- Keith Beven
Papers from G. Schoups's perspective
Ordered by how strongly the researchers G. Schoups trusts endorse each paper. Same corpus, a different order for every researcher.
- Hydrological forecasting uncertainty assessment: Incoherence of the GLUE methodology
- The future of distributed models: model calibration and uncertainty prediction.
- Effective and efficient global optimization for conceptual rainfall‐runoff models
- Towards a Bayesian total error analysis of conceptual rainfall-runoff models: Characterising model error using storm-dependent parameters
- A Markov Chain Monte Carlo Scheme for parameter estimation and inference in conceptual rainfall‐runoff modeling
- Bayesian recursive parameter estimation for hydrologic models
- Accelerating Markov Chain Monte Carlo Simulation by Differential Evolution with Self-Adaptive Randomized Subspace Sampling
- An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction
- Monte Carlo Sampling Methods Using Markov Chains and Their Applications
- An Analysis of Transformations
Their papers
- Corruption of accuracy and efficiency of Markov chain Monte Carlo simulation by inaccurate numerical implementation of conceptual hydrologic models (2010) — 83 citations
- Why hydrological predictions should be evaluated using information theory (2010) — 127 citations
- A formal likelihood function for parameter and predictive inference of hydrologic models with correlated, heteroscedastic, and non‐Gaussian errors (2010) — 570 citations