Parametric Signal Models: Matlab Practice

Related lectures (32)

Covers image processing techniques including noise addition, filtering, and image enhancement using various filters and tools.

Covers essential MATLAB functions, variables, loops, and debugging tools.

Explores harmonic signals, spectrum estimation, and signal analysis methods using MATLAB tools.

Covers logical arrays, 3D surface plotting, parametric curves, interpolation, and fitting in Matlab.

Explores parametric estimation, integrated processes, seasonal modeling, and ARIMA model building in time series analysis.

Explores parametric signal models, including AR processes and Markov chains, covering synthesis, analysis, and correlation structures.

Explores multivariate time series analysis, cointegration, forecasting with ARMA models, and practical applications in interest rates analysis.

Explores autoregressive models for time series analysis, covering AR(1), AR(2), identification, and MA models.

Covers count data models and Poisson regression, then transitions to univariate time series analysis for forecasting economic variables.

Covers the analysis and modeling of univariate time series, focusing on stationarity, ARMA processes, and forecasting.

Covers parametric estimation, seasonal modeling, Box-Jenkins methods, variance calculations, and dependence measures in time series analysis.

Explores the Van der Pol oscillator, its dynamic equations, and phase plane representation.

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