A Gentle Introduction to Maximum Likelihood Estimation for Machine Learning
Last Updated on November 5, 2019
Density estimation is the problem of estimating the probability distribution for a sample of observations from a problem domain.
There are many techniques for solving density estimation, although a common framework used throughout the field of machine learning is maximum likelihood estimation. Maximum likelihood estimation involves defining a likelihood function for calculating the conditional probability of observing the data sample given a probability distribution and distribution parameters. This approach can be used to search a space of possible distributions and parameters.
This flexible probabilistic framework also provides the foundation for many machine learning algorithms, including important methods such as linear regression and logistic regression for predicting numeric values and class labels respectively, but also more generally for deep learning artificial neural networks.
In this post, you will discover a gentle introduction to maximum likelihood estimation.
After reading this post, you will know:
- Maximum Likelihood Estimation is a probabilistic framework for solving the problem of density estimation.
- It involves maximizing a likelihood function in order to find the probability distribution and parameters that best explain the observed data.
- It provides a framework for predictive modeling in machine learning where finding model parameters can
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