Probabilistic inference of multi‐Gaussian fields from indirect hydrological data using circulant embedding and dimensionality reduction
By Eric Laloy, Niklas Linde, Diederik Jacques, Jasper A. Vrugt. 2015
47 citations recorded by the source corpus.
Subfields: Environmental Engineering, Geophysics, Ocean Engineering
View Paper: https://www.semanticscholar.org/paper/b4bae1f966e44036f093b71482f2e2ebd285e4ce
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
In Geophysics
In Ocean Engineering
Citations in the corpus
- Training‐Image Based Geostatistical Inversion Using a Spatial Generative Adversarial Neural Network (2017, background): “Various studies in hydrology and geophysics (amongst others) have shown that DREAM (ZS) can derive posterior distributions with 25-250 dimensions (e.g., Laloy et al. , 2012, 2013; Linde and Vrugt , 2013; Laloy et al. , 2015; Lochbühler et al. , 2015).”
- Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation (2016, unknown): “…experiments [e.g. (Vrugt et al., 2008a, 2009; Laloy and Vrugt, 2012a; Laloy et116 al., 2013; Linde and Vrugt, 2013; Lochbühler et al., 2014; Laloy et al., 2015)] have shown that DREAM is superior to other adaptive MCMC sampling118 approaches, and in high-dimensional search/variable spaces…”