Lecture

Maximum Likelihood Estimation

Description

This lecture introduces the concept of maximum likelihood estimation using a simple example of predicting car ownership based on age. The instructor explains how to calculate the likelihood function, use it to estimate parameters, and the properties of maximum likelihood estimation. The lecture also covers the log likelihood, consistency, unbiasedness, efficiency, and hypothesis testing.

In MOOCs (2)

Instructor

tempor magna

Velit eu nostrud veniam eiusmod eu in Lorem ad tempor enim do aliqua nisi deserunt. Pariatur nisi ipsum aute esse non culpa. Ullamco amet non incididunt fugiat. Nulla reprehenderit ipsum officia cupidatat est ut culpa do in enim culpa pariatur ullamco id. Do irure consectetur aute consequat aliqua labore reprehenderit consectetur consequat amet duis aliquip amet elit. Lorem fugiat sunt commodo do aute tempor labore.

Official source

https://mediaspace.epfl.ch/media/0_ghaeaz1c

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 (35)

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.