Linear Estimation & Prediction: Models & Methods

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Related lectures (32)

Detection & Estimation

Covers the fundamentals of detection and estimation theory, focusing on mean-squared error and hypothesis testing.

Basics of Linear Regression

Covers the basics of linear regression, including OLS estimators, hypothesis testing, and confidence intervals.

Linear Estimation and Prediction: Part 2

Covers the estimation and prediction of random signals in linear systems.

Linear Regression: Basics and Estimation

Covers the basics of linear regression and how to solve estimation problems using least squares and matrix notation.

Elements of Statistics: Probability, Distributions, and Estimation

Covers probability theory, distributions, and estimation in statistics, emphasizing accuracy, precision, and resolution of measurements.

Matlab: Interactive Mode and Project Steps

Introduces Matlab basics, error handling, and billiards project concepts.

Linear Prediction and Filtering: Part 2

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

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Statistical Signal & Data Processing

Explores Statistical Signal & Data Processing fundamentals, tools, and applications in various fields.

Linear Prediction and Estimation

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

Linear Regression: Statistical Inference Perspective

Explores linear regression from a statistical inference perspective, covering probabilistic models, ground truth, labels, and maximum likelihood estimators.

Spectral Estimation in Time Series

Covers spectral estimation in time series analysis, including imaging kernels, tapering methods, and AR models.

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