Kalman Filter: Time Series

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Explores structural modelling in time series and introduces the Kalman filter for prediction and estimation.

Covers parametric signal models and practical Matlab applications for Markov chains and AutoRegressive processes.

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Explores linear estimation and prediction in AR parametric models, focusing on Yule Walker equations and Wiener filter.

Explores statistical models, parameter estimation, and sampling distributions in probability and statistics.

Explores linear estimation, Wiener filters, and optimal prediction in signal processing.

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

Explores estimators, bias, and efficiency in statistics, emphasizing the trade-off between bias and variability.

Introduces Fisher Information for parameter estimation based on collected data.

Explores bias, variance, unbiased estimators, and confidence intervals in statistical estimation.