Lecture

Bayesian Inference: Gaussian Prior for Mean

In course

DEMO: reprehenderit incididunt magna tempor

Ullamco reprehenderit est enim id laborum. Anim anim culpa laborum reprehenderit consectetur ea fugiat esse. Amet qui exercitation adipisicing cillum sint eu esse minim irure. Eu eu officia labore magna dolore ad eu.

Description

This lecture covers Bayesian inference for the mean of a Gaussian distribution, assuming the variance is known. It discusses the posterior mean, posterior variance, and the maximum a posteriori (MAP) estimator. The lecture also explains the concept of independence vs. conditional independence and the consecutive updates of the posterior distribution.

Instructor

Cupidatat culpa qui amet adipisicing. Cupidatat non exercitation cillum elit sint aute aliquip exercitation id esse ullamco nulla mollit. Incididunt aute pariatur aute sint officia minim sint do aliqua minim est exercitation. Nulla incididunt veniam mollit ad sunt minim mollit officia.

Official source

https://mediaspace.epfl.ch/media/0_ak3jq5kn

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, Statistical hypothesis testing

Related lectures (37)

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.