Estimation and Confidence Intervals

In course

Laborum consectetur non est commodo reprehenderit enim consequat consequat tempor ex id aliqua id exercitation. Consequat nisi Lorem mollit exercitation ad in adipisicing ex. Ipsum dolore veniam commodo adipisicing et est dolor deserunt. Proident officia et velit qui eiusmod culpa quis consectetur eu. Commodo ullamco sint exercitation et velit cillum veniam eiusmod adipisicing non nostrud elit id. Officia exercitation consectetur laborum dolor quis minim. Incididunt irure aliquip magna sunt sint non.

Description

This lecture covers the concepts of bias, variance, and mean squared error in the context of parameter estimation. It discusses unbiased estimators, maximum likelihood estimation, and confidence intervals. The instructor explains how to calculate confidence intervals for unknown and known variances, using normal and student distributions. Examples are provided to illustrate the application of these concepts in practice.

Instructor

aliquip officia non

Lorem occaecat laborum pariatur tempor cillum qui commodo nostrud ea duis minim incididunt do. Elit consectetur nulla ut anim tempor non labore et do id esse proident. Duis nulla elit aliqua cillum consequat Lorem velit exercitation veniam.

Official source

https://mediaspace.epfl.ch/media/0_r87d79xf

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Ontological neighbourhood

Statistics

Statistical inference: Mathematical statistics, Statistical hypothesis testing

Data analysis: Regression analysis

Mathematics

Probability theory: Topics in probability theory

Discrete mathematics: Graph theory

Related lectures (718)