Explores signal processing fundamentals, including discrete time signals, spectral factorization, and stochastic processes.
Explores dependence, correlation, and conditional expectations in probability and statistics, highlighting their significance and limitations.
Covers correlation and cross-correlations in air pollution data analysis, including time series, autocorrelations, Fourier analysis, and power spectrum.
Explores dependence in random vectors, covering joint density, conditional independence, covariance, and moment generating functions.
Explores independence and covariance between random variables, discussing their implications and calculation methods.
Explores multivariate methods like PCA, SVD, PLS, and ICA for dimensionality reduction in functional brain imaging.
Introduces key statistical concepts like probability, random variables, and correlation, with examples and explanations.
