Linear Estimation and Prediction

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Related lectures (32)

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Linear Estimation & Prediction: Models & Methods

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

Parametric Signal Models: Matlab Practice

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

Linear Prediction and Estimation

Explores linear prediction, optimal filters, random signals, stationarity, autocorrelation, power spectral density, and Fourier transform in signal processing.

Signal Processing Fundamentals

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

Linear Prediction and Filtering: Part 2

Explores linear prediction, prediction coefficients, mean squared error minimization, and the Levinson-Durbin algorithm in signal processing.

Kalman Filter: Introduction

Introduces the Kalman Filter, a method to estimate system state from noisy measurements.

Signal Processing: Noise Filtering and Signal Estimation

Explores noise filtering, signal estimation, and signal-to-noise ratio optimization through Wiener-Khintchine theorem and power spectral density.

Kalman Filter: Time Series

Covers structural modeling, state space models, and the Kalman filter in time series analysis.

Stochastic Processes and Spectral Densities

Covers spectral densities, signal correlations, and white noise processes in stochastic systems.

Neural Signals and Signal Processing

Explores neural signal processing for brain-computer interfaces, including decoding techniques like Kalman filters and spike sorting.