Lecture

Maximum Likelihood, MSE, Fisher Information, Cramér-Rao Bound

In course
DEMO: veniam nulla amet
Laboris ad esse eu non velit consectetur. Commodo excepteur sit cillum est elit mollit fugiat consectetur. Eiusmod sit id voluptate nostrud proident excepteur ex mollit enim nisi excepteur. In dolore sint amet ullamco in sint excepteur id dolor mollit est cillum voluptate. Labore magna qui mollit ut dolore cillum aliquip mollit adipisicing amet voluptate non cupidatat.
Login to see this section
Description

This lecture covers topics such as maximum likelihood estimation, mean squared error, Fisher information, and the Cramér-Rao bound. It explains how to calculate these metrics and their significance in statistical inference.

Instructor
laborum est
Eu sit culpa minim id velit ipsum ea. Minim non et eu proident pariatur ea dolor proident magna. Incididunt ea eu ea irure ipsum voluptate adipisicing ea consectetur incididunt laboris officia. Incididunt eiusmod dolore et sit. Ea dolore magna exercitation enim aute aute nulla tempor mollit laboris eu. Ipsum nulla qui esse occaecat voluptate pariatur id excepteur excepteur aliqua excepteur do occaecat. Eiusmod aliquip pariatur mollit sint aute commodo enim mollit non non consequat aute anim irure.
Login to see this section
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.
Related lectures (35)
Detection & Estimation
Covers the fundamentals of detection and estimation theory, focusing on mean-squared error and hypothesis testing.
Maximum Likelihood Estimation
Introduces maximum likelihood estimation for statistical parameter estimation, covering bias, variance, and mean squared error.
Estimators and Bias
Explores estimators, bias, and efficiency in statistics, emphasizing the trade-off between bias and variability.
Sampling Distributions: Estimators and Variance
Covers estimation of parameters, MSE, Fisher information, and the Rao-Blackwell Theorem.
Statistical Estimation
Explores statistical estimation, comparing estimators based on mean and variance, and delving into mean squared error and Cramér-Rao bound.
Show more

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.