Search results for "STATISTICS"
showing 10 items of 7671 documents
Group Metropolis Sampling
2017
Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. Two well-known class of MC methods are the Importance Sampling (IS) techniques and the Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce the Group Importance Sampling (GIS) framework where different sets of weighted samples are properly summarized with one summary particle and one summary weight. GIS facilitates the design of novel efficient MC techniques. For instance, we present the Group Metropolis Sampling (GMS) algorithm which produces a Markov chain of sets of weighted samples. GMS in general outperforms other multiple try schemes…
Recycling Gibbs sampling
2017
Gibbs sampling is a well-known Markov chain Monte Carlo (MCMC) algorithm, extensively used in signal processing, machine learning and statistics. The key point for the successful application of the Gibbs sampler is the ability to draw samples from the full-conditional probability density functions efficiently. In the general case this is not possible, so in order to speed up the convergence of the chain, it is required to generate auxiliary samples. However, such intermediate information is finally disregarded. In this work, we show that these auxiliary samples can be recycled within the Gibbs estimators, improving their efficiency with no extra cost. Theoretical and exhaustive numerical co…
Machine learning techniques demonstrating individual movement patterns of the vertebral column: the fingerprint of spinal motion
2022
Surface topography systems enable the capture of spinal dynamic movement; however, it is unclear whether vertebral dynamics are unique enough to identify individuals. Therefore, in this study, we investigated whether the identification of individuals is possible based on dynamic spinal data. Three different data representations were compared (automated extracted features using contrastive loss and triplet loss functions, as well as simple descriptive statistics). High accuracies indicated the possible existence of a personal spinal 'fingerprint', therefore enabling subject recognition. The present work forms the basis for an objective comparison of subjects and the transfer of the method to…
Structured Output SVM for Remote Sensing Image Classification
2011
Traditional kernel classifiers assume independence among the classification outputs. As a consequence, each misclassification receives the same weight in the loss function. Moreover, the kernel function only takes into account the similarity between input values and ignores possible relationships between the classes to be predicted. These assumptions are not consistent for most of real-life problems. In the particular case of remote sensing data, this is not a good assumption either. Segmentation of images acquired by airborne or satellite sensors is a very active field of research in which one tries to classify a pixel into a predefined set of classes of interest (e.g. water, grass, trees,…
Error mitigation using RaptorQ codes in an experimental indoor free space optical link under the influence of turbulence
2015
In free space optical (FSO) communications, several factors can strongly affect the link quality. Among them, one of the most important impairments that can degrade the FSO link quality and its reliability even under the clear sky conditions consists of optical turbulence. In this work, the authors investigate the generation of both weak and moderate turbulence regimes in an indoor environment to assess the FSO link quality. In particular, they show that, due to the presence of the turbulence, the link experiences both erasure errors and packet losses during transmission, and also compare the experimental statistical distribution of samples with the predicted Gamma Gamma model. Furthermore,…
Imbalance Effects in the Lucas Model: An Analytical Exploration
2004
In this note, we use a technique analogous to Xie's method (1994) to solve analytically the Lucas model with externality in a specific parametric case. In particular, we characterize the shape of imbalance effects in this model. Our results are entirely consistent with the findings of the related computational literature. Moreover, our analytical investigation tends to show that these findings are robust to the presence of the Lucas externality as long as a unique equilibrium path exist.
Design of composite measure schemes for comparative severity assessment in animal-based neuroscience research: A case study focussed on rat epilepsy …
2020
PLOS ONE 15(5), e0230141 (2020). doi:10.1371/journal.pone.0230141
Clinically-Driven Virtual Patient Cohorts Generation: An Application to Aorta
2021
The combination of machine learning methods together with computational modeling and simulation of the cardiovascular system brings the possibility of obtaining very valuable information about new therapies or clinical devices through in-silico experiments. However, the application of machine learning methods demands access to large cohorts of patients. As an alternative to medical data acquisition and processing, which often requires some degree of manual intervention, the generation of virtual cohorts made of synthetic patients can be automated. However, the generation of a synthetic sample can still be computationally demanding to guarantee that it is clinically meaningful and that it re…
Sleep assessment devices: types, market analysis, and a critical view on accuracy and validation
2019
[EN] Introduction: Sleep assessment devices are essential for the detection, diagnosis, and monitoring of sleep disorders. This paper provides a state-of-the-art review and comparison of sleep assessment devices and a market analysis. Areas covered: Hardware devices are classified into contact and contactless devices. For each group, the underlying technologies are presented, paying special attention to their limitations. A systematic literature review has been carried out by comparing the most important validation studies of sleep tracking devices in terms of sensitivity and specificity. A market analysis has also been carried out in order to list the most used, best-selling, and most high…
CovSel
2018
Ensemble methods combine the predictions of a set of models to reach a better prediction quality compared to a single model's prediction. The ensemble process consists of three steps: 1) the generation phase where the models are created, 2) the selection phase where a set of possible ensembles is composed and one is selected by a selection method, 3) the fusion phase where the individual models' predictions of the selected ensemble are combined to an ensemble's estimate. This paper proposes CovSel, a selection approach for regression problems that ranks ensembles based on the coverage of adequately estimated training points and selects the ensemble with the highest coverage to be used in th…