# 5. Maximum Likelihood Estimation (cont.) | Summary and Q&A

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August 17, 2017
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MIT OpenCourseWare
5. Maximum Likelihood Estimation (cont.)

## TL;DR

Maximum Likelihood Estimation is a statistical method that involves maximizing the likelihood function, often represented by the log-likelihood, to estimate the parameters of a statistical model.

## Key Insights

• 🧑‍💻 Maximizing the likelihood function is equivalent to minimizing the negative log-likelihood function.
• 🧑‍💻 The log-likelihood function is often used instead of the likelihood function because it simplifies the optimization process.

## Transcript

The following content is provided under a Creative Commons license. Your support will help MIT OpenCourseWare continue to offer high quality educational resources for free. To make a donation or to view additional materials from hundreds of MIT courses, visit MIT OpenCourseWare at ocw.mit.edu. PROFESSOR: So I'm using a few things here, right? I'm u... Read More

### Q: What is the likelihood function?

The likelihood function is a function that measures how likely a set of observed data is given certain model parameters.

### Q: Why is the log-likelihood often used instead of the likelihood?

The log-likelihood function is often used because it simplifies the optimization process and has the same maximum as the likelihood function.

### Q: What is the Fisher information?

The Fisher information is a measure of the curvature of the likelihood function and can be used to quantify the amount of information that the data contains about the model parameters.

## Summary & Key Takeaways

• Maximum Likelihood Estimation (MLE) involves maximizing the likelihood function, which is a function of the observed data and the model parameters.

• MLE can be used to estimate parameters for various statistical models, such as Bernoulli trials, Poisson distribution, and Gaussian distribution.

• The log-likelihood function is often used instead of the likelihood function because it simplifies the optimization process.