vbICA code
2020
- 2,753Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Usage2,753
- Views1,555
- 1,555
- Downloads1,198
- 1,198
Dataset Description
The independent component analysis (ICA) is a popular technique adopted to approach the so-called blind source separation (BSS) problem, i.e., the problem of recovering and separating the original sources that generate the observed data. However, the independence condition is not easy to impose, and it is often necessary to introduce some approximations. Here we provide a MATLAB code which implement a modified variational Bayesian ICA (vbICA) method for the analysis GNSS time series. The vbICA method models the probability density function (pdf) of each source signal using a mix of Gaussian distributions, allowing for more flexibility in the description of the pdf of the sources with respect to standard ICA, and giving a more reliable estimate of them. In particular, this method allows to recover the multiple sources of ground deformation even in the presence of missing data. This material is based on the original work of Choudrey (2002) and Choudrey and Roberts (2003), subsequently...
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