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Explores Gaussian Mixture Models for data classification, focusing on denoising signals and estimating original data using likelihood and posteriori approaches.
Explores optimal testing methods in statistics, focusing on the Neyman-Pearson framework and the construction of test functions for different types of hypotheses.
Delves into measuring learning effects in digital education and analytics, covering research questions, variables, experimental design, and bias solutions.
