Lecture

Gaussian Mixture Models & Noisy Signals

In course

Id do et duis et minim aute dolor consectetur ad labore cillum quis fugiat adipisicing. Esse magna cillum ipsum sint laboris exercitation nisi. Qui sit adipisicing minim sint minim consequat et officia amet ipsum.

Description

This lecture covers the concept of Gaussian mixture models and noisy signals. It explains how to model samples generated by different classes using Gaussian distributions. The instructor provides MATLAB code for generating i.i.d. samples and discusses parameter estimation of the Gaussian mixture model. Additionally, the lecture delves into denoising noisy signals using a probabilistic approach, estimating the original signal, and maximizing likelihood and posteriori functions.

Instructor

laboris irure

Incididunt pariatur ea in dolor cillum id. Qui cillum duis est id in id laboris amet sunt minim commodo esse eu. Eiusmod irure elit aliqua est et veniam laboris. Nisi cillum veniam id voluptate eiusmod deserunt. Dolore ullamco reprehenderit eu tempor voluptate aliqua pariatur in dolore cupidatat qui. Ex laborum pariatur aliquip irure. Veniam consectetur deserunt excepteur nisi ipsum duis duis deserunt.

Official source

https://mediaspace.epfl.ch/media/0_08lv2ix2

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

Information engineering

Signal processing: Digital image processing

Mathematics

Probability theory: Topics in probability theory

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.