Kalman Filter: Minimal Variance Estimator

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Covers fundamental statistics concepts, including estimation theory, distributions, and the law of large numbers, with practical examples.

Explores Kalman filtering theory, focusing on innovations, predictions, and practical applications in estimating vehicle position and velocity.

Introduces the basics of probability models, covering random variables, distributions, and statistical estimation.

Explores Yule Walker equations for efficient implementation and correlation analysis in signal processing.

Explores the Red bus/Blue bus paradox, nested logit models, and multivariate extreme value models in transportation.

Explores signal processing fundamentals, including discrete time signals, spectral factorization, and stochastic processes.

Discusses the steady-state Kalman predictor, provides examples, and compares Luenberger and Kalman filtering.

Explores dependence in random vectors, covering joint density, conditional independence, covariance, and moment generating functions.

Explores the Method of Moments, Bias-Variance tradeoff, Consistency, Plug-In Principle, and Likelihood Principle in point estimation.

Explores estimation measures of performance, including the Cramér-Rao bound and maximum likelihood estimation.