Importance sampling (IS) is a powerful Monte Carlo methodology for the
a...
Reinforcement Learning has drawn huge interest as a tool for solving opt...
Earth observation from satellites offers the possibility to monitor our
...
In this work, we analyze the noisy importance sampling (IS), i.e., IS wo...
In many inference problems, the evaluation of complex and costly models ...
Bayesian models have become very popular over the last years in several
...
The modelling of Earth observation data is a challenging problem, typica...
Importance sampling (IS) is a Monte Carlo technique for the approximatio...
Earth observation from satellite sensory data poses challenging problems...
Atmospheric correction of Earth Observation data is one of the most crit...
Most problems in Earth sciences aim to do inferences about the system, w...
The expressive power of Bayesian kernel-based methods has led them to be...
This is an up-to-date introduction to, and overview of, marginal likelih...
Importance sampling (IS) and numerical integration methods are usually
e...
Many fields of science and engineering rely on running simulations with
...
A large number and diversity of techniques have been offered in the
lite...
The effective sample size (ESS) is widely used in sample-based simulatio...
Many applications in signal processing require the estimation of some
pa...
Solving inverse problems is central to geosciences and remote sensing.
R...
Monte Carlo (MC) methods have become very popular in signal processing d...
Monte Carlo (MC) sampling methods are widely applied in Bayesian inferen...
The number of methods available for classification of multi-label data h...
Multi-dimensional classification (MDC) is the supervised learning proble...