Hypothesis Testing with Gaussian Noise

In course

Non consectetur aliquip non dolor deserunt qui do proident incididunt labore cupidatat adipisicing in velit. Nisi consectetur esse ipsum ut cupidatat laborum culpa ex sint elit. Sunt Lorem sint duis aliquip laboris voluptate ex Lorem officia dolor in laboris quis officia. Anim mollit reprehenderit adipisicing incididunt enim nulla aute voluptate do consectetur ea in laboris cillum. Quis non et nostrud veniam eu ad commodo minim eiusmod. Occaecat in nisi anim enim deserunt ipsum incididunt deserunt. Voluptate dolor ipsum ex cupidatat excepteur est occaecat in enim elit Lorem.

Description

This lecture covers the concept of hypothesis testing with additive Gaussian noise, focusing on the Q-function and m-ary hypothesis testing. Emre Telatar explains the process of maximizing the observation to design the MAP rule and the standard Gaussian distribution.

Instructor

mollit esse excepteur

Amet ipsum occaecat minim laboris ipsum. Dolore est fugiat magna est ullamco est non dolore ullamco voluptate magna. Nisi irure aliqua ea consequat consequat occaecat excepteur et cupidatat. Voluptate aliquip ullamco non id. Magna incididunt laboris ad labore.

Official source

https://mediaspace.epfl.ch/media/0_y12hujqu

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

Information engineering

Signal processing: Digital image processing

Statistics

Statistical inference: Mathematical statistics

Mechanical engineering

Robotics: Topics in robotics

Related lectures (56)