Maximum a Posteriori (MAP) estimation is a popular method used in machine learning and statistics to estimate the parameters of a statistical model. It combines prior knowledge about the distribution of the parameters with data to produce an estimate that is likely to be the most accurate given the information available.
In MAP estimation, we use Bayes’ theorem to compute the posterior distribution of the parameters given the data and prior information. This is done by multiplying the likelihood of the data given the parameters by the prior distribution of the parameters. The estimate of the parameters is then taken to be the maximum of the posterior distribution.
MAP estimation is often used in machine learning when we have a model with many parameters and limited data. In this case, the prior information can be used to regularize the model and prevent overfitting. The prior information can also be used to incorporate domain knowledge into the model and make it more robust.
One of the advantages of MAP estimation is that it provides a way to combine prior knowledge and data in a systematic way. It also provides a way to specify the uncertainty in the estimates, which can be useful for model selection and other purposes.
Where to Learn More#
We apply MAP estimation in the following courses:
Bayesian Machine Learning in Python: A/B Testing
Deep Learning Prerequisites: Linear Regression in Python
Deep Learning Prerequisites: Logistic Regression in Python
Data Science: Deep Learning and Neural Networks in Python
Recommender Systems and Deep Learning in Python