Search Results for author:
Found 5 papers, 0 papers with code
Let's consider more general nonlinear approaches to study teleconnections of climate variables
We argue that this method is essentially a particular case of the method called rotated complex kernel principal component analysis (ROCK-PCA) introduced in (Bueso et al., 2019, 2020), where we proposed the same approach: first transform the data to the complex plane with the Hilbert transform and then apply the varimax rotation, with the only difference that the eigendecomposition is performed in the dual (kernel) Hilbert space.
Statistical Learning for End-to-End Simulations
This work evaluates the accuracy and computation cost of interpolation and emulation methods to sample the input LUT variable space.
Machine Learning Information Fusion in Earth Observation: A Comprehensive Review of Methods, Applications and Data Sources
This paper reviews the most important information fusion data-driven algorithms based on Machine Learning (ML) techniques for problems in Earth observation.
Deep Importance Sampling based on Regression for Model Inversion and Emulation
For this reason, several adaptive importance sampling (AIS) schemes have been proposed in the literature.
Group Importance Sampling for Particle Filtering and MCMC
Importance Sampling (IS) is a well-known Monte Carlo technique that approximates integrals involving a posterior distribution by means of weighted samples.
