Maximum likelihood estimation

Travis chat

Filters

Maximum Likelihood Estimation (MLE) is a statistical method used for estimating the parameters of a statistical model. The core idea behind MLE is to find the parameter values that maximize the likelihood function, which measures how well the model explains the observed data. Essentially, MLE seeks the parameter values that make the observed data most probable.

To perform MLE, you start by defining the likelihood function based on your data and the assumed distribution. For example, if you are working with a Poisson distribution, the likelihood function will depend on the parameter λ, which represents the average number of occurrences in a given interval. You then take the logarithm of the likelihood function (log-likelihood) to simplify the calculations. The next step involves differentiating the log-likelihood with respect to the parameter and setting the result equal to zero to find the maximum ³⁴.

MLE is widely used in various fields, including machine learning, to fit models like logistic regression and to make inferences about population parameters based on sample data ²⁵.

Towards Data Science

This is part two in a series on fundamental concepts of machine learning. We discuss the basics of modeling data and fitting parameters with Maximum Likelihood Estimation.

Maximum Likelihood the Easy Way

Towards Data Science

This article’s will first demonstrate Maximum Likelihood Estimation (MLE) using a simple example. Then, we will build on the first example fitting a logistic regression model using MLE. By…

Understanding Maximum Likelihood Estimation

Towards Data Science

Let’s say you collect some data from some distribution. As you might know, each distribution is just a function with some inputs. If you change the value of these inputs, the outputs will change…

Parameter Inference — Maximum Likelihood

Towards Data Science

This post takes an in-depth tour in one of the most important concepts of theoretical Machine Learning, viz., Parameter Inference. I will try to focus on an intuitive understanding of the concept…

A Gentle Introduction to Maximum Likelihood Estimation

Towards Data Science

The first time I heard someone use the term maximum likelihood estimation, I went to Google and found out what it meant. Then I went to Wikipedia to find out what it really meant. I got this: To…

Maximum Likelihood Estimation for Beginners (with R code)

Towards Data Science

Intuitive explanation of maximum likelihood method The maximum likelihood principle is a fundamental method of estimation for a large number of models in data science, machine learning, and artificia...

Maximum Likelihood Estimation in R

Towards Data Science

Often, you’ll have some level of intuition — or perhaps concrete evidence — to suggest that a set of observations has been generated by a particular statistical distribution. Similar phenomena to the…...

Maximum Likelihood

Dive intro Deep Learning Book

One of the most commonly encountered way of thinking in machine learning is the maximum likelihood point of view. This is the concept that when working with a probabilistic model with unknown paramete...

Maximum Likelihood Estimation of Parameters for Random Variables

Towards Data Science

C oncepts in probability and statistics can be somewhat elusive due to the combination of high level mathematics, bad notation, and entanglement of random variables and data. This article sheds light ...

Maximum Likelihood Classification

Towards Data Science

The main idea of Maximum Likelihood Classification is to predict the class label y that maximizes the likelihood of our observed data x. We will consider x as being a random vector and y as being a…

Maximum Likelihood Estimation -Conceptual understanding using an example

Analytics Vidhya

Maximum Likelihood Estimation (MLE) is one of the core concepts of Machine Learning. A lot of other Machine Learning algorithms/techniques are based on results derived using MLE. Therefore, it is…

Calculating Maximum Likelihood Estimation by Hand Step-by-step

Towards Data Science

I wrote this because I couldn’t find many tutorials showing the detailed math for this calculation. So I decided to write it out thoroughly and show all the steps in case people who also haven't done…...