Question

What is MLE?Describe with examples?

Answer 1

In this post I’ll explain what the maximum likelihood method for parameter estimation is and go through a simple example to demonstrate the method. Some of the content requires knowledge of fundamental probability concepts such as the definition of joint probability and independence of events. I’ve written a blog post with these prerequisites so feel free to read this if you think you need a refresher.

Answer 2

Answer:

According machine Learning, MLE stands for Maximum Likelihood Estimation.

The goal of Maximum Likelihood is to find the the optimal way to fit a distribution to the data. There are lots of different types of distributions for different types of data.

Example of applying the MLE :  

let x1, ....., xn be a random sample from u( 0, theta ) , theta > 0. find MLE of theta. show that it is not unbiased.

solution:   f( x ) = 1 / theta ;  0 < x < theta

  the likelihood function is,

  ( theta |  x ) = { 1 / theta ^ n  ; if 0 <= xi <= theta

                           otherwise 0         ;

                      =  { 1 /  theta ^ n  ; if 0 <= x₁ <= theta

                             otherwise 0