Sensitivity analysis and assessment of prior model probabilities in MLBMA with application to unsaturated fractured tuff

By Ming Ye, Shlomo P. Neuman, P. Meyer, Karl Pohlmann. 2005

102 citations recorded by the source corpus.

Subfields: Environmental Engineering, Mechanical Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/9f3ad426b2e0f2dc5db36d44cf8fda6236875f54

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. Ming Ye
2. Hoshin V. Gupta
3. H. Dai
4. Yuqiong Liu
5. P. Meyer
6. Xiaobao Li
7. Frank T.-C. Tsai
8. Jasper A. Vrugt

In Mechanical Engineering

1. Xiaobao Li
2. Frank T.-C. Tsai
3. Ming Ye
4. Shlomo P. Neuman
5. M. G. Schaap
6. R. Khaleel
7. H. Deng

In Ocean Engineering

1. Ming Ye
2. Hoshin V. Gupta
3. H. Dai
4. Xiaobao Li
5. Frank T.-C. Tsai
6. P. Meyer
7. M. G. Schaap
8. R. Khaleel

Citations in the corpus

• Inverse groundwater modeling for hydraulic conductivity estimation using Bayesian model averaging and variance window (2008, methodology): “Prior model probabilities were also suggested to be identified through the entropy maximization method [Ye et al., 2005].”
• On model selection criteria in multimodel analysis (2007, methodology): “Neuman [2003] proposed using, and Ye et al. [2004, 2005] as well as Meyer et al. [2007] have implemented, KIC in the context of maximum likelihood BMA (MLBMA).”
• Variance-based global sensitivity analysis for multiple scenarios and models with implementation using sparse grid collocation (2015, background): “Following Ye et al. (2005), the minimum and maximum probabilities of a single model are set as 5% and 85%, respectively, and the probability increment is 5%.”
• Quantification of uncertainty in pedotransfer function‐based parameter estimation for unsaturated flow modeling (2009, methodology): “This is due to the fact that movement of the injected water is hindered by the second layer of fine material, and the moisture content below the 15 m depth essentially remains unchanged during the 2000 injection experiment [Ye et al., 2005b].”
• Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework (2007, background): “Neuman [2002, 2003] proposed a maximum likelihood version of BMA (MLBMA) that proves to be more computationally feasible and capable of dealing with situations where reliable prior information is lacking [Ye et al., 2004, 2005].”