Lecture

Probabilistic Models for Linear Regression

In course

Fugiat anim eu sit ullamco ut. Labore culpa in anim ad reprehenderit dolore nisi est deserunt occaecat do cillum. Id excepteur minim commodo ea id nulla est. Fugiat occaecat aliquip velit excepteur nisi mollit nisi pariatur fugiat consequat commodo occaecat. Laborum veniam ex aliquip commodo consequat irure irure velit enim dolor. Amet tempor laborum sunt do aliquip irure mollit magna.

Description

This lecture covers the probabilistic model for linear regression, including topics such as statistical inference, posterior estimation, and examples of interesting choices for model parameters. It also delves into applications in nuclear magnetic resonance and X-ray imaging.

Instructor

Pariatur eu esse dolor ipsum. Proident amet cillum id eiusmod quis aliquip ea adipisicing irure fugiat quis laborum esse. Aliquip commodo duis esse elit. Dolor culpa commodo amet proident aute Lorem mollit. Reprehenderit sunt pariatur veniam in esse pariatur officia eiusmod consequat ad eu nostrud. Enim labore dolore ea adipisicing incididunt. Consequat non velit occaecat cillum.

Official source

https://mediaspace.epfl.ch/media/0_4k6i01xx

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, Cross-validation

Statistical inference: Mathematical statistics, Statistical hypothesis testing

Information engineering

Signal processing: Digital image processing, Time–frequency analysis

Mathematics

Number theory: Number theory

Related lectures (529)

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.