What is Maximum a Posteriori Estimation?

Maximum a posteriori (MAP) estimation is a method of estimating the parameters of a statistical model. It is a type of Bayesian estimation, which uses Bayes' theorem to update the probability for a hypothesis as more evidence or information becomes available.

MAP estimation can be used in machine learning and data science to estimate the parameters of a model, such as the weights of a neural network or the coefficients of a linear regression model. MAP estimation can also be used to estimate the probability of a given hypothesis, such as the probability of a given data point belonging to a certain class.

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