Lecture

Gaussian Conditional Model and Properties

In course

Dolore qui consequat culpa excepteur non velit magna elit irure ex exercitation. In incididunt quis fugiat anim et magna reprehenderit aute. Labore officia sit eu amet reprehenderit nulla et non sit fugiat commodo anim commodo.

Description

This lecture covers the Gaussian conditional model for linear regression, the properties of the model if the data is Gaussian, and the paradox of Simpson illustrated with the example of kidney stone treatment comparison. It also discusses the normal equations, linear vs affine regression, and the maximum likelihood estimation principle.

Instructor

Lorem minim officia

Nulla aute adipisicing labore ullamco proident culpa velit. Laborum non ipsum tempor est do ex aute ad mollit sint duis eiusmod consequat. Eu dolor ea in reprehenderit officia in officia incididunt minim dolor eiusmod velit Lorem.

Official source

https://mediaspace.epfl.ch/media/0_66h4rszw

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

Data analysis: Regression analysis

Statistical inference: Mathematical statistics

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.