
Compressed particle methods for expensive models with application in Astronomy and Remote Sensing
In many inference problems, the evaluation of complex and costly models ...
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Compressed Monte Carlo with application in particle filtering
Bayesian models have become very popular over the last years in several ...
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Integrating Domain Knowledge in Datadriven Earth Observation with Process Convolutions
The modelling of Earth observation data is a challenging problem, typica...
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Advances in Importance Sampling
Importance sampling (IS) is a Monte Carlo technique for the approximatio...
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PhysicsAware Gaussian Processes in Remote Sensing
Earth observation from satellite sensory data poses challenging problems...
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Gradientbased Automatic LookUp Table Generator for Atmospheric Radiative Transfer Models
Atmospheric correction of Earth Observation data is one of the most crit...
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Living in the Physics and Machine Learning Interplay for Earth Observation
Most problems in Earth sciences aim to do inferences about the system, w...
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Joint introduction to Gaussian Processes and Relevance Vector Machines with Connections to Kalman filtering and other Kernel Smoothers
The expressive power of Bayesian kernelbased methods has led them to be...
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Marginal likelihood computation for model selection and hypothesis testing: an extensive review
This is an uptodate introduction to, and overview of, marginal likelih...
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Importance Gaussian Quadrature
Importance sampling (IS) and numerical integration methods are usually e...
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Active emulation of computer codes with Gaussian processes – Application to remote sensing
Many fields of science and engineering rely on running simulations with ...
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Probabilistic Regressor Chains with Monte Carlo Methods
A large number and diversity of techniques have been offered in the lite...
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Rethinking the Effective Sample Size
The effective sample size (ESS) is widely used in samplebased simulatio...
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A Review of Multiple Try MCMC algorithms for Signal Processing
Many applications in signal processing require the estimation of some pa...
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Joint Gaussian Processes for Biophysical Parameter Retrieval
Solving inverse problems is central to geosciences and remote sensing. R...
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Parsimonious Adaptive Rejection Sampling
Monte Carlo (MC) methods have become very popular in signal processing d...
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Metropolis Sampling
Monte Carlo (MC) sampling methods are widely applied in Bayesian inferen...
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Multilabel Methods for Prediction with Sequential Data
The number of methods available for classification of multilabel data h...
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Efficient Monte Carlo Methods for MultiDimensional Learning with Classifier Chains
Multidimensional classification (MDC) is the supervised learning proble...
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Luca Martino
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