Lecture

Fisher Information, Cramér-Rao Inequality, MLE

Description

This lecture covers the definition of the score function, Fisher information, and Cramér-Rao inequality. It explains how the Fisher information measures the amount of information in a sample and how the Cramér-Rao inequality provides a lower bound on the variance of unbiased estimators. The invariance of the maximum likelihood estimator (MLE) under bijections is also discussed, along with the asymptotics of the MLE obtained from i.i.d. observations.

Instructor

incididunt quis pariatur nisi

Qui reprehenderit enim enim qui culpa nisi. Mollit amet laborum nulla deserunt. Ullamco pariatur officia Lorem irure officia eu est sunt amet voluptate ipsum exercitation. Ad id culpa do dolor culpa. Occaecat occaecat duis anim ad eu mollit ex magna qui. Dolore tempor tempor labore esse proident sunt nisi irure magna aliqua occaecat. Aliquip ea eiusmod dolor non.

Official source

https://mediaspace.epfl.ch/media/0_x040yjjl

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 (40)

Graph Chatbot

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.