Lecture

Central Limit Theorem: Empirical Mean

Description

This lecture covers the Law of Large Numbers (LLN) and the Central Limit Theorem (CLT) applied to the empirical mean of Bernoulli and Uniform random variables. It explains the convergence in distribution, the CLT for i.i.d. random variables, and the multivariate version of the CLT. The slides illustrate the convergence of the empirical mean distribution around the true mean, emphasizing the Gaussian distribution. The continuous mapping theorem is also discussed in the context of the CLT.

Instructor

proident commodo labore

Cillum quis ea reprehenderit tempor duis minim magna id. Aute id Lorem minim aute nostrud dolore qui eu nisi nisi minim. Exercitation sunt nulla ut aliquip qui ipsum amet et sit nulla adipisicing Lorem.

Official source

https://mediaspace.epfl.ch/media/0_nii930f8

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

Related lectures (42)

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.