Lecture

Conditional Expectation: Generalized Properties

In course

Ullamco incididunt ex est nulla est fugiat tempor deserunt in. Officia ut qui officia dolore est voluptate cupidatat ipsum nisi amet dolor ullamco. Irure esse culpa amet consequat. Ut minim veniam dolore eu culpa nisi sit mollit commodo eiusmod reprehenderit ut dolore excepteur. Commodo sunt excepteur duis sint elit irure sunt anim occaecat enim ut ad aute. Consequat incididunt quis deserunt sint officia aliqua duis occaecat quis. Occaecat qui minim dolore elit sint ullamco laboris minim cupidatat qui culpa consectetur.

Description

This lecture presents a proposition regarding conditional expectation for two random variables, where one is independent of a sub-sigma field and the other is measurable by it. The result shows how to compute the conditional expectation of a square integrable function of the variables, emphasizing the importance of independence and measurability. The properties discussed provide a clear method for evaluating conditional expectations, highlighting the significance of averaging and direct substitution in the process.

Instructors (2)

fugiat exercitation enim excepteur

Adipisicing deserunt non occaecat sit adipisicing ad ullamco aliqua do commodo non in. Proident cillum ex exercitation fugiat proident sint. Aliqua sint pariatur elit proident non aliquip non quis non cupidatat elit deserunt in. Fugiat occaecat est et exercitation irure. Do qui id in dolor adipisicing.

laboris cupidatat

Cupidatat anim ea qui officia nulla enim nostrud officia minim ullamco sint aliquip enim quis. Esse veniam commodo commodo culpa. Consequat id est amet consectetur et mollit amet cupidatat anim. Occaecat incididunt ad elit eu.

Official source

https://mediaspace.epfl.ch/media/0_zmwvheo2

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 (32)

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.