N. Bulygina
Water Science and Technology · 2009–2011
A researcher in the Science corpus: 2 papers; trusts 31 researchers and is trusted by 16 researchers here, as inferred from the citation graph.
Subfields
- Water Science and Technology — 19 citations into it; #113 of 1,711 by trust here, #1,062 by citations
- Environmental Engineering — 16 citations into it; #2,930 of 5,479 by trust here, #4,524 by citations
- Global and Planetary Change — 13 citations into it; #989 of 10,225 by trust here, #6,997 by citations
- Statistics and Probability — 7 citations into it
- Artificial Intelligence — 5 citations into it
Whom N. Bulygina trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Hoshin V. Gupta
- Alan E. Gelfand
- Shlomo P. Neuman
- Bernard Walter Silverman
- Thomas S. Ferguson
- Huaiyu Zhu
- Soroosh Sorooshian
- Dmitri Kavetski
- George A. Kuczera
- Adrian F. M. Smith
Who trusts N. Bulygina overall
- Hoshin V. Gupta
- M. Clark
- Wei Gong
- Weihua Li
- Dmitri Kavetski
- Jasper A. Vrugt
- C. Prieto
- Benjamin Renard
- A. Sankarasubramanian
- Mojtaba Sadegh
Papers from N. Bulygina's perspective
Ordered by how strongly the researchers N. Bulygina 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
- Bayesian recursive parameter estimation for hydrologic models
- Maximum likelihood Bayesian averaging of uncertain model predictions
- Sampling-Based Approaches to Calculating Marginal Densities
- Towards a Bayesian total error analysis of conceptual rainfall-runoff models: Characterising model error using storm-dependent parameters
- Dual state-parameter estimation of hydrological models using ensemble Kalman filter
- Optimal use of the SCE-UA global optimization method for calibrating watershed models
- Bayesian calibration of computer models
- Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter