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Results: 83
Number of items: 83
  • Open Access
    Partridge, D. G., Vrugt, J. A., Tunved, P., Ekman, A. M. L., Struthers, H., & Sooroshian, A. (2012). Inverse modeling of cloud-aerosol interactions -- Part 2: Sensitivity tests on liquid phase clouds using a Markov Chain Monte Carlo based simulation approach. Atmospheric Chemistry and Physics, 12(6), 2823-2847. https://doi.org/10.5194/acp-12-2823-2012
  • Open Access
    Lalov, E., Linde, N., & Vrugt, J. A. (2012). Mass conservative three-dimensional water tracer distribution from MCMC inversion of time-lapse GPR data. Water Resources Research, 48(7), WR011238. https://doi.org/10.1029/2011WR011238
  • Open Access
    Gupta, H. V., Clark, M. P., Vrugt, J. A., Abramowitz, G., & Ye, M. (2012). Towards a comprehensive assessment of model structural adequacy. Water Resources Research, 48(8), W08301. https://doi.org/10.1029/2011WR011044
  • Open Access
    Rings, J., Vrugt, J. A., Schoups, G., Huisman, J. A., & Vereecken, H. (2012). Bayesian model averaging using particle filtering and Gaussian mixture modeling: theory, concepts, and simulation experiments. Water Resources Research, 48, WR011607. https://doi.org/10.1029/2011WR011607
  • Open Access
    Laloy, E., & Vrugt, J. A. (2012). High-dimensional posterior exploration of hydrologic models using multiple-try DREAM(ZS) and high-performance computing. Water Resources Research, 48(1), W01526. https://doi.org/10.1029/2011WR010608
  • Wöhling, T., & Vrugt, J. A. (2011). Multiresponse multilayer vadose zone model calibration using Markov chain Monte Carlo simulation and field water retention data. Water Resources Research, 47. https://doi.org/10.1029/2010WR009265
  • Dane, J. H., Vrugt, J. A., & Unsal, E. (2011). Soil hydraulic functions determined from measurements of air permeability, capillary modeling, and high-dimensional parameter estimation. Vadose Zone Journal, 10(1), 459-465. https://doi.org/10.2136/vzj2010.0053
  • He, M., Hogue, T. S., Franz, K. J., Margulis, S. A., & Vrugt, J. A. (2011). Corruption of parameter behavior and regionalization by model and forcing data errors: a Bayesian example using the SNOW17 model. Water Resources Research, 47. https://doi.org/10.1029/2010WR009753
  • He, M., Hogue, T. S., Franz, K. J., Margulis, S. A., & Vrugt, J. A. (2011). Characterizing parameter sensitivity and uncertainty for a snow model across hydroclimatic regimes. Advances in Water Resources, 34(1), 114-127. https://doi.org/10.1016/j.advwatres.2010.10.002
  • Minasny, B., Vrugt, J. A., & McBratney, A. B. (2011). Confronting uncertainty in model-based geostatistics using Markov Chain Monte Carlo simulation. Geoderma, 163(3-4), 150-153. https://doi.org/10.1016/j.geoderma.2011.03.011
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