Concept

Estimation

Related lectures (30)

Explores conditional expectation properties, variance, and examples in practical applications.

Explores sampling distributions, estimation methods, and consistency in parameter estimation.

Covers the design of estimators and controllers for multivariable systems, focusing on state estimation and disturbance rejection.

Introduces the rigorous approach to sensor fusion for modern applications.

Covers the theorems of superposition and colouring for Poisson processes.

Covers the calculation of cumulative distribution functions and probability density functions for transformed random variables.

Explains how q = rw defines a Poisson process and its intensity.

Explores the Kalman filter as a minimal variance estimator and its application in estimating position and velocity.

Explains estimation through mean-squared error and Fisher information in the context of adaptive filters and exponentiated distributions.

Explores methods for detecting and correcting parameter errors in power grids using statistical analysis and computational tools.

Covers the estimation of R using bivariate normal distributions and explores the marginal and conditional distributions of X₁ and X₂.

Covers the topic of estimating R and calculating probabilities and expected waiting times.

Covers the principles of linear MM SE estimation and the minimization of errors in linear regression.

Discusses bias and variance in statistical estimation, exploring the trade-off between accuracy and variability.

Explains estimation, correlation, and Pearson correlation in statistics, focusing on measuring and describing relationships between variables.

Explores the linearized and extended Kalman Filters, illustrating their application in nonlinear systems and the estimation of unknown parameters.

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

Covers the concept of mean-square-error inference and optimal estimators for inference problems using different design criteria.

Explores the Wiener filter for signal estimation and adaptive denoising in scenarios with non-changing properties and real-time constraints.

Explores estimation, forecasting, and model comparison in time series analysis using real data examples to motivate the study.