Kalman Filtering: State Estimation and Prediction

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Explores Galilean transformations in spacetime, focusing on measurements and coordinate transformations.

Discusses variance and covariance calculations, transformations of random variables, and mixtures in probability and statistics.

Explores isometries in Euclidean spaces, including translations, rotations, and linear symmetries, with a focus on matrices.

Explores state estimation and Kalman filtering for multivariable control systems, with applications in communication channels and vehicle navigation.

Provides an overview of image formation and transformation principles in computer vision.

Explores statistical models, families of distributions, transformations, and their applications in probability theory.

Covers the concept of affine transformations in functions, including shifting and compressing functions.

Explores maximum likelihood estimation and multivariate hypothesis testing, including challenges and strategies for testing multiple hypotheses.

Explores the georeferencing of images, covering methodology steps, transformations, resampling, and practical application using QGIS software.

Covers the theory and applications of Principal Component Analysis, focusing on dimension reduction and eigenvectors.