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Gauss mixtures have gained popularity in statistics and statistical signal processing applications for a variety of reasons, including their ability to well approximate a large class of interesting densities and the availability of algorithms such as the Baum Welch or expectation-maximization (EM) algorithm for constructing the models based on observed data.
Monte Carlo methods have become essential tools to solve complex Bayesian inference problems in different fields, such as computational statistics, machine learning, and statistical signal processing.
Mathematical models incorporate wavelet transform (WT), time frequency approaches, Fourier transform, statistical signal analysis, and higher-order statistics.
We derived statistical signal models of RF impairments, and based on the statistical signal models, we designed estimators of RF impairments.
Developments in statistical signal processing using principal component and independent component analysis are also considered.
Model-based statistical signal processing and decision theoretic methods for FDI are presented.
There is an adaptive fusion method, a rule based method, and a statistical signal method.
This would suggest that statistical signal analysis may be useful to classify these templates.
Her research area includes statistical signal processing and applied machine learning.
The estimation problem is a topic of great interest in the statistical signal processing community.
Statistical signal processing is adopted to detect signals with average strength much lower than one photon per measurement.
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