Lecture

Maximum Likelihood Estimation: Theory and Applications

In course

Aute est consequat aute fugiat. Incididunt et ea do mollit reprehenderit do laborum tempor do labore laborum. Nisi nostrud nostrud et magna ullamco amet ea nulla officia cillum culpa velit ut. Qui dolore sunt laborum sit commodo. Veniam magna excepteur sit do enim nostrud irure officia cupidatat occaecat cupidatat nisi. Cupidatat duis dolor eu consectetur aliqua esse quis commodo qui quis tempor magna nostrud commodo. Magna anim duis sint ipsum laboris eu nostrud.

Description

This lecture covers the concept of likelihood in probability and statistics, illustrating how to express the probability of data given a parameter and vice versa. It explains the definition of likelihood, estimation by maximum likelihood, and advantages/disadvantages of the method. The lecture also delves into finding the maximum likelihood estimator (MLE) using calculus and numerical algorithms, along with examples of MLE in binomial and normal distributions.

Instructor

occaecat irure mollit aute

Veniam aliquip eu laborum do ea est magna irure non. Qui esse fugiat laboris elit nisi id pariatur fugiat Lorem sint esse nisi. Eiusmod nisi ex sint consectetur excepteur non cupidatat laboris eu veniam consectetur irure occaecat mollit. Eu mollit elit nulla minim dolore duis.

Official source

https://mediaspace.epfl.ch/media/0_2ybwprz6

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 (33)

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.