Maximum entropy spectral estimation

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Related lectures (31)

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Fast Fourier Transform (FFT): Lecture 4

Covers the Fast Fourier Transform (FFT) algorithm, interpolation, filters, image processing, and experimental techniques in TEM and STM.

Spectral analysis and stationarity tests

Covers spectral analysis, stationarity tests, challenges of nonstationary time series, and tools for analysis with missing data.

Laser Noise: Understanding Laser Line Width and Coherence

Explores laser noise, emphasizing laser line width and coherence for precise measurements in amplitude and phase quadrature.

Time Series: Spectral Estimation & Yule Walker

On Time Series explores Spectral Estimation, Yule Walker method, and ARIMA models.

Power Spectral Density Computation

Covers the computation of power spectral density and the design of communication systems.

Stochastic Models for Communications: Continuous-Time Linear Systems

Covers continuous-time stochastic processes in linear systems, including signal analysis and filtering.

Spectral & Parametric Estimation: Time Series

Covers spectral estimation techniques like tapering and parametric estimation, emphasizing the importance of AR models and Whittle likelihood in time series analysis.

Multivariate Time Series and Spectral Representation

Explores multivariate time series analysis, emphasizing spectral representation and estimation methods.

Signal Processing: Basics and Spectral Analysis

Covers the basics of signal processing, linear estimation, and digital filters.

Signal Processing Fundamentals

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