Search results for "Gaussian process"
showing 10 items of 128 documents
Hierarchical Bayesian models for analysing fish biomass data. An application to Parapenaeus longirostris biomass data
2022
The Mediterranean International Trawl Survey (MEDITS) programme provides spatially referenced ecological data. We adopted a hierarchical Bayesian model to analyse Parapenaeus longirostris biomass data. The model comprises three parts, each of which identifies: the variability due to the explanatory variables, the variability due to the spatial domain (seen as a Gaussian Process) and the irregular component modelled as white noise. The estimated parameters show that some seabed characteristics affect biomass quantity and that the estimated behaviour of the Gaussian Process changes over different groups of years.
Introducing ARTMO's Machine-Learning Classification Algorithms Toolbox: Application to Plant-Type Detection in a Semi-Steppe Iranian Landscape.
2022
Accurate plant-type (PT) detection forms an important basis for sustainable land management maintaining biodiversity and ecosystem services. In this sense, Sentinel-2 satellite images of the Copernicus program offer spatial, spectral, temporal, and radiometric characteristics with great potential for mapping and monitoring PTs. In addition, the selection of a best-performing algorithm needs to be considered for obtaining PT classification as accurate as possible . To date, no freely downloadable toolbox exists that brings the diversity of the latest supervised machine-learning classification algorithms (MLCAs) together into a single intuitive user-friendly graphical user interface (GUI). To…
Biophysical parameter retrieval with warped Gaussian processes
2015
This paper focuses on biophysical parameter retrieval based on Gaussian Processes (GPs). Very often an arbitrary transformation is applied to the observed variable (e.g. chlorophyll content) to better pose the problem. This standard practice essentially tries to linearize/uniformize the distribution by applying non-linear link functions like the logarithmic, the exponential or the logistic functions. In this paper, we propose to use a GP model that automatically learns the optimal transformation directly from the data. The so-called warped GP regression (WGPR) presented in [1] models output observations as a parametric nonlinear transformation of a GP. The parameters of such prior model are…
Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods – A comparison
2015
Abstract Given the forthcoming availability of Sentinel-2 (S2) images, this paper provides a systematic comparison of retrieval accuracy and processing speed of a multitude of parametric, non-parametric and physically-based retrieval methods using simulated S2 data. An experimental field dataset (SPARC), collected at the agricultural site of Barrax (Spain), was used to evaluate different retrieval methods on their ability to estimate leaf area index (LAI). With regard to parametric methods, all possible band combinations for several two-band and three-band index formulations and a linear regression fitting function have been evaluated. From a set of over ten thousand indices evaluated, the …
Passive millimeter wave image classification with large scale Gaussian processes
2017
Passive Millimeter Wave Images (PMMWIs) are being increasingly used to identify and localize objects concealed under clothing. Taking into account the quality of these images and the unknown position, shape, and size of the hidden objects, large data sets are required to build successful classification/detection systems. Kernel methods, in particular Gaussian Processes (GPs), are sound, flexible, and popular techniques to address supervised learning problems. Unfortunately, their computational cost is known to be prohibitive for large scale applications. In this work, we present a novel approach to PMMWI classification based on the use of Gaussian Processes for large data sets. The proposed…
Biophysical parameter estimation with adaptive Gaussian Processes
2009
We evaluate Gaussian Processes (GPs) for the estimation of biophysical parameters from acquired multispectral data. The standard GP formulation is used, and all hyperparameters (kernel parameters and noise variance) are optimized by maximizing the marginal likelihood. This gives rise to a fully-adaptive GP to data characteristics, both in terms of signal and noise properties. The good numerical results in the estimation of oceanic chlorophyll concentration and leaf membrane state confirm GPs as adequate, alternative non-parametric methods for biophysical parameter estimation. GPs are also analyzed by scrutinizing the predictive variance, the estimated noise variance, and the relevance of ea…
Statistical Properties of Double Hoyt Fading With Applications to the Performance Analysis of Wireless Communication Systems
2018
In this paper, we investigate the statistical properties of double Hoyt fading channels, where the overall received signal is determined by the product of two statistically independent but not necessarily identically distributed single Hoyt processes. Finite-range integral expressions are first derived for the probability density function (PDF), cumulative distribution function (CDF), level-crossing rate (LCR), and average duration of fades of the envelope fading process. A closed-form approximate solution is also deduced for the LCR by making use of the Laplace approximation theorem. Applying the derived PDF of the double Hoyt channel, we then provide analytical expressions for the average…
Information Decomposition in Multivariate Systems: Definitions, Implementation and Application to Cardiovascular Networks
2016
The continuously growing framework of information dynamics encompasses a set of tools, rooted in information theory and statistical physics, which allow to quantify different aspects of the statistical structure of multivariate processes reflecting the temporal dynamics of complex networks. Building on the most recent developments in this field, this work designs a complete approach to dissect the information carried by the target of a network of multiple interacting systems into the new information produced by the system, the information stored in the system, and the information transferred to it from the other systems; information storage and transfer are then further decomposed into amou…
Level-Crossing Rate and Average Duration of Fades in Non-Isotropic Hoyt Fading Channels with Applications to Selection Combining Diversity
2015
In this paper, we investigate the second-order statistics of Hoyt fading channels under non isotropic scattering scenarios. Assuming an asymmetrical Doppler power spectral density (PSD), we derive, in the form of single finite-range integrals, expressions for the level-crossing rate (LCR) and average duration of fades (ADF). These new results are then applied to obtain the LCR and ADF of selection combining (SC) diversity over non-isotropic Hoyt channels. In addition to their importance for studying the system performance and characterizing the dynamic behavior of multipath fading channels, the formulas derived are general in that they can be applied to many non-isotropic scattering situati…
Iterative closure method for non-linear systems driven by polynomials of Gaussian filtered processes
2008
This paper concerns the statistical characterization of the non-Gaussian response of non-linear systems excited by polynomial forms of filtered Gaussian processes. The non-Gaussianity requires the computation of moments of any order. The problem is solved profiting from both the stochastic equivalent linearization (EL), and the moment equation approach of Ito's stochastic differential calculus through a procedure divided into two parts. The first step requires the linearization of the system, while retaining the non-linear excitation; the response statistical moments are calculated exactly, and constitute a first estimate of the moments of the actual non-linear system. In the second step, t…