Kalman Filter: Time Series

Related lectures (31)

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Explores the criteria for good estimators, emphasizing consistency and efficiency in estimation.

Explores linear estimation, optimal criteria, and the orthogonality principle for good choices in estimation.

Explains statistical estimators for random variables and Gaussian distributions, focusing on error functions for integration.

Addresses overfitting in supervised learning through polynomial regression case studies and model selection techniques.

Explores bias, variance, and confidence intervals in parameter estimation using examples and distributions.

Explores the bias-variance tradeoff in ridge estimation, showcasing how a bit of bias can enhance mean squared error by reducing variance.

Explores probabilistic estimation in the Spin Glass Card game, emphasizing room splitting based on a given parameter.

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Covers estimation of parameters, MSE, Fisher information, and the Rao-Blackwell Theorem.

Discusses the bias-variance tradeoff in machine learning, emphasizing the balance between model complexity and prediction accuracy.