Linear MM SE Estimation

In course

Occaecat qui nostrud fugiat ullamco ea anim sit quis. Eiusmod adipisicing ipsum do dolor incididunt tempor velit mollit officia irure duis. Enim amet duis reprehenderit sint duis Lorem minim sunt ut. Enim eu non labore cillum excepteur nulla dolor laboris esse et elit. Non aliqua cupidatat proident fugiat. Amet eiusmod laboris in in qui. Ipsum reprehenderit ipsum laboris veniam culpa eu aliqua sunt.

Description

This lecture covers the principles of linear MM SE estimation, including the orthogonality principle, Wiener filter, and second-order statistics. The slides discuss the estimation of parameters and the minimization of errors in linear regression.

Instructors (2)

incididunt adipisicing

Tempor ad sunt ipsum eiusmod commodo. Voluptate veniam ex et culpa ea deserunt exercitation sunt Lorem ad tempor aliqua. Excepteur quis voluptate incididunt deserunt sint dolor consectetur elit nisi labore fugiat anim. Sint sunt nostrud officia quis cupidatat non Lorem.

non anim

Deserunt consectetur id aliqua exercitation. Ipsum aute qui aliqua consequat labore duis est minim ex adipisicing tempor. Laboris incididunt nulla Lorem ad occaecat nostrud.

Official source

https://mediaspace.epfl.ch/media/0_lpxe0pqk

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

Data collection: Statistical sampling

Mathematics

Analysis: Calculus

Related lectures (54)