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Found 20 papers, 2 papers with code
CAMEO: Curiosity Augmented Metropolis for Exploratory Optimal Policies
We further show that the different policies we sample present different risk profiles, corresponding to interesting practical applications in interpretability, and represents a first step towards learning the distribution of optimal policies itself.
Inference over radiative transfer models using variational and expectation maximization methods
Radiative transfer models (RTMs) encode the energy transfer through the atmosphere, and are used to model and understand the Earth system, as well as to estimate the parameters that describe the status of the Earth from satellite observations by inverse modeling.
Optimality in Noisy Importance Sampling
In this work, we analyze the noisy importance sampling (IS), i. e., IS working with noisy evaluations of the target density.
Compressed Monte Carlo with application in particle filtering
In its basic version, C-MC is strictly related to the stratification technique, a well-known method used for variance reduction purposes.
Compressed particle methods for expensive models with application in Astronomy and Remote Sensing
In many inference problems, the evaluation of complex and costly models is often required.
Integrating Domain Knowledge in Data-driven Earth Observation with Process Convolutions
We specifically propose the use of a class of GP convolution models called latent force models (LFMs) for EO time series modelling, analysis and understanding.
Interpolation and Gap Filling of Landsat Reflectance Time Series
Products derived from a single multispectral sensor are hampered by a limited spatial, spectral or temporal resolutions.
Physics-Aware Gaussian Processes in Remote Sensing
However, GPs are typically used for inverse modeling based on concurrent observations and in situ measurements only.
Gradient-based Automatic Look-Up Table Generator for Atmospheric Radiative Transfer Models
Our results indicate that, when compared to a pseudo-random homogeneous distribution of the LUT nodes, GALGA reduces (1) the LUT size by $\sim$75\% and (2) the maximum interpolation relative errors by 0. 5\% It is concluded that automatic LUT design might benefit from the methodology proposed in GALGA to reduce computation time and interpolation errors.
Living in the Physics and Machine Learning Interplay for Earth Observation
Most problems in Earth sciences aim to do inferences about the system, where accurate predictions are just a tiny part of the whole problem.
A Joint introduction to Gaussian Processes and Relevance Vector Machines with Connections to Kalman filtering and other Kernel Smoothers
Our focus is on developing a common framework with which to view these methods, via intermediate methods a probabilistic version of the well-known kernel ridge regression, and drawing connections among them, via dual formulations, and discussion of their application in the context of major tasks: regression, smoothing, interpolation, and filtering.
Marginal likelihood computation for model selection and hypothesis testing: an extensive review
This is an up-to-date introduction to, and overview of, marginal likelihood computation for model selection and hypothesis testing.
Active emulation of computer codes with Gaussian processes -- Application to remote sensing
Many fields of science and engineering rely on running simulations with complex and computationally expensive models to understand the involved processes in the system of interest.
Probabilistic Regressor Chains with Monte Carlo Methods
A large number and diversity of techniques have been offered in the literature in recent years for solving multi-label classification tasks, including classifier chains where predictions are cascaded to other models as additional features.
A Review of Multiple Try MCMC algorithms for Signal Processing
Many applications in signal processing require the estimation of some parameters of interest given a set of observed data.
Joint Gaussian Processes for Biophysical Parameter Retrieval
Radiative transfer models (RTMs) represent mathematically the physical laws which govern the phenomena in remote sensing applications (forward models).
Metropolis Sampling
Monte Carlo (MC) sampling methods are widely applied in Bayesian inference, system simulation and optimization problems.
The Recycling Gibbs Sampler for Efficient Learning
The key point for the successful application of the Gibbs sampler is the ability to draw efficiently samples from the full-conditional probability density functions.
Multi-label Methods for Prediction with Sequential Data
In this paper we detect and elaborate on connections between multi-label methods and Markovian models, and study the suitability of multi-label methods for prediction in sequential data.
Efficient Monte Carlo Methods for Multi-Dimensional Learning with Classifier Chains
Multi-dimensional classification (MDC) is the supervised learning problem where an instance is associated with multiple classes, rather than with a single class, as in traditional classification problems.
