Concept

Estimation

Related lectures (30)

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

Multivariable Control: State Estimation and Disturbance Rejection

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

Kalman Filter: Minimal Variance Estimator

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

Estimation: Mean-Squared Error and Fisher Information

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

Linear MM SE Estimation

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

Bias and Variance in Estimation

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

Kalman Filter: Linearized vs Extended

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

Mean-Square-Error Inference

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

Estimation and Forecasting in Time Series

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

Panel data: serial correlation MOOC: Selected Topics on Discrete Choice

Explores the panel effect model with fixed and random effects, discussing estimation challenges and the impact of serial correlation.

Box-Jenkins Methodology: Building Time Series Models

Covers the Box-Jenkins methodology for building time series models, including model identification, variance calculations, and model diagnostics.

Density of States and Bayesian Inference in Computational Mathematics

Explores computing density of states and Bayesian inference using importance sampling, showcasing lower variance and parallelizability of the proposed method.

Model Selection Criteria: AIC, BIC, Cp

Explores model selection criteria like AIC, BIC, and Cp in statistics for data science.

Estimation Methods in Probability and Statistics

Discusses estimation methods in probability and statistics, focusing on maximum likelihood estimation and confidence intervals.

Conditional Expectation: Properties and Examples Log in to Mediaspace to watch this video

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

Sensor Orientation: Introduction to Sensor Fusion Log in to Mediaspace to watch this video

Introduces the rigorous approach to sensor fusion for modern applications.

Mapping and Colouring: Poisson Processes Log in to Mediaspace to watch this video

Covers the theorems of superposition and colouring for Poisson processes.

Transformations of Variables Log in to Mediaspace to watch this video

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

Poisson Process Mapping Log in to Mediaspace to watch this video

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

Detecting and Correcting Parameter Errors in Power Grids Log in to Mediaspace to watch this video

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