Articles | Volume 2, issue 2
https://doi.org/10.5194/gmd-2-137-2009
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/gmd-2-137-2009
© Author(s) 2009. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
28 Sep 2009
Bayesian calibration of the Thermosphere-Ionosphere Electrodynamics General Circulation Model (TIE-GCM)
S. Guillas
Department of Statistical Science & Aon Benfield UCL Hazard Research Centre, University College London, London, UK
J. Rougier
Department of Mathematics, University of Bristol, Bristol, UK
A. Maute
High Altitude Observatory, National Center for Atmospheric Research, Boulder CO, USA
A. D. Richmond
High Altitude Observatory, National Center for Atmospheric Research, Boulder CO, USA
C. D. Linkletter
Center for Statistical Sciences, Brown University, Providence, RI, USA
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- Guidelines for the Bayesian calibration of building energy models A. Chong & K. Menberg 10.1016/j.enbuild.2018.06.028
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10 citations as recorded by crossref.
- Calibration of Dynamic Building Energy Models with Multiple Responses Using Bayesian Inference and Linear Regression Models Q. Li et al. 10.1016/j.egypro.2015.11.037
- Multi-fidelity approach to Bayesian parameter estimation in subsurface heat and fluid transport models K. Menberg et al. 10.1016/j.scitotenv.2020.140846
- The influence and adjust method of hyperparameters’ prior distributions in Bayesian calibration for building stock energy prediction Y. Wang et al. 10.1016/j.enbuild.2022.112413
- Bayesian calibration of building energy models with large datasets A. Chong et al. 10.1016/j.enbuild.2017.08.069
- Guidelines for the Bayesian calibration of building energy models A. Chong & K. Menberg 10.1016/j.enbuild.2018.06.028
- Continuous calibration of a digital twin: Comparison of particle filter and Bayesian calibration approaches R. Ward et al. 10.1017/dce.2021.12
- Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods M. Girolami & B. Calderhead 10.1111/j.1467-9868.2010.00765.x
- An iterative process for efficient optimisation of parameters in geoscientific models: a demonstration using the Parallel Ice Sheet Model (PISM) version 0.7.3 S. Phipps et al. 10.5194/gmd-14-5107-2021
- Bayesian parameter inference for shallow subsurface modeling using field data and impacts on geothermal planning M. Kreitmair et al. 10.1017/dce.2022.32
- Influence of error terms in Bayesian calibration of energy system models K. Menberg et al. 10.1080/19401493.2018.1475506
4 citations as recorded by crossref.
- Review on stochastic modeling methods for building stock energy prediction H. Lim & Z. Zhai 10.1007/s12273-017-0383-y
- Assessment of linear emulators in lightweight Bayesian calibration of dynamic building energy models for parameter estimation and performance prediction Q. Li et al. 10.1016/j.enbuild.2016.04.025
- Comprehensive evaluation of the influence of meta-models on Bayesian calibration H. Lim & Z. Zhai 10.1016/j.enbuild.2017.09.009
- Bayesian inference of structural error in inverse models of thermal response tests W. Choi et al. 10.1016/j.apenergy.2018.06.147
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