research
∙
10/19/2022
Gradient-based Adaptive Importance Samplers
Importance sampling (IS) is a powerful Monte Carlo methodology for the a...
share
research
∙
05/19/2022
CAMEO: Curiosity Augmented Metropolis for Exploratory Optimal Policies
Reinforcement Learning has drawn huge interest as a tool for solving opt...
research
∙
04/07/2022
Inference over radiative transfer models using variational and expectation maximization methods
Earth observation from satellites offers the possibility to monitor our ...
research
∙
01/07/2022
Optimality in Noisy Importance Sampling
In this work, we analyze the noisy importance sampling (IS), i.e., IS wo...
research
∙
07/18/2021
Compressed particle methods for expensive models with application in Astronomy and Remote Sensing
In many inference problems, the evaluation of complex and costly models ...
research
∙
07/18/2021
Compressed Monte Carlo with application in particle filtering
Bayesian models have become very popular over the last years in several ...
research
∙
04/16/2021
Integrating Domain Knowledge in Data-driven Earth Observation with Process Convolutions
The modelling of Earth observation data is a challenging problem, typica...
research
∙
02/10/2021
Advances in Importance Sampling
Importance sampling (IS) is a Monte Carlo technique for the approximatio...
research
∙
12/07/2020
Physics-Aware Gaussian Processes in Remote Sensing
Earth observation from satellite sensory data poses challenging problems...
research
∙
12/07/2020
Gradient-based Automatic Look-Up Table Generator for Atmospheric Radiative Transfer Models
Atmospheric correction of Earth Observation data is one of the most crit...
research
∙
10/18/2020
Living in the Physics and Machine Learning Interplay for Earth Observation
Most problems in Earth sciences aim to do inferences about the system, w...
research
∙
09/19/2020
Joint introduction to Gaussian Processes and Relevance Vector Machines with Connections to Kalman filtering and other Kernel Smoothers
The expressive power of Bayesian kernel-based methods has led them to be...
research
∙
05/17/2020
Marginal likelihood computation for model selection and hypothesis testing: an extensive review
This is an up-to-date introduction to, and overview of, marginal likelih...
research
∙
01/09/2020
Importance Gaussian Quadrature
Importance sampling (IS) and numerical integration methods are usually e...
research
∙
12/13/2019
Active emulation of computer codes with Gaussian processes – Application to remote sensing
Many fields of science and engineering rely on running simulations with ...
research
∙
07/18/2019
Probabilistic Regressor Chains with Monte Carlo Methods
A large number and diversity of techniques have been offered in the lite...
research
∙
09/11/2018
Rethinking the Effective Sample Size
The effective sample size (ESS) is widely used in sample-based simulatio...
research
∙
01/27/2018
A Review of Multiple Try MCMC algorithms for Signal Processing
Many applications in signal processing require the estimation of some pa...
research
∙
11/14/2017
Joint Gaussian Processes for Biophysical Parameter Retrieval
Solving inverse problems is central to geosciences and remote sensing. R...
research
∙
10/13/2017
Parsimonious Adaptive Rejection Sampling
Monte Carlo (MC) methods have become very popular in signal processing d...
research
∙
04/15/2017
Metropolis Sampling
Monte Carlo (MC) sampling methods are widely applied in Bayesian inferen...
research
∙
09/27/2016
Multi-label Methods for Prediction with Sequential Data
The number of methods available for classification of multi-label data h...
research
∙
11/09/2012
