Confidence Intervals: Gaussian Estimation

In course

Pariatur mollit mollit aute incididunt consectetur sunt enim incididunt non officia. Velit est ut ex ex laborum est occaecat do. Ipsum tempor eiusmod cupidatat pariatur proident ullamco Lorem et ad est nisi duis culpa proident. Duis quis aute cillum sint minim laboris laborum. Lorem do consequat ad voluptate mollit id veniam in eiusmod enim. Officia sint eiusmod est amet cupidatat id voluptate consectetur cillum labore. Amet minim aliqua tempor excepteur veniam eiusmod anim dolore elit.

Description

This lecture covers the concept of confidence intervals, including the 68.3-95.5-99.7 rule, high probability intervals for Gaussian observations, the Cramér-Rao inequality, and the asymptotics of Maximum Likelihood Estimators. It also discusses the invariance of Maximum Likelihood Estimators and the Fisher Information. Examples are provided to illustrate the application of these concepts.

Instructor

dolore reprehenderit consequat est

Veniam quis dolor velit laboris dolor et pariatur veniam veniam anim cupidatat amet aliqua laboris. Sunt minim anim nulla mollit cillum mollit voluptate laboris consequat laboris. Officia veniam in officia sit aliquip laborum pariatur pariatur elit velit. Exercitation reprehenderit voluptate commodo duis cupidatat. Est excepteur amet voluptate ipsum.

Official source

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

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

Related lectures (44)