Autocovariance

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Related lectures (31)

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Structural Modelling and the Kalman Filter: Time Series

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

Wiener Process: Definition and Properties

Explains the definition and properties of the Wiener process, focusing on its covariance function and continuous paths.

Explicit Stabilised Methods: Applications to Bayesian Inverse Problems

Explores explicit stabilised Runge-Kutta methods and their application to Bayesian inverse problems, covering optimization, sampling, and numerical experiments.

Gaussian Process: Covariance and Correlation Functions

Explores Gaussian processes, covariance functions, intrinsic stationarity, and extreme applications in statistics.

Signal Processing Fundamentals

Covers the basics of signal processing, including auto covariance function, power spectral density, and filter design.

Frequency Estimation of Noisy Random Signals

Explores digital experiments for frequency estimation of random signals, including shot noise, Brownian motion, and harmonic signals in noise.

Linear Estimation and Prediction

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

Frequency estimation for stochastic signals (large noise)

Covers numerical experiments for frequency estimation of stochastic signals.

Functional Factor Models: Forecasting Mortality Curves in Japan

Explores high-dimensional functional factor models for forecasting mortality curves in Japan, discussing estimation, consistency, and application.

Point Processes: Convergence and Gaussian Processes

Covers point processes, convergence criteria, Laplace functionals, Gaussian processes, covariance functions, and intrinsic stationarity.