课程: Probability Foundations for Data Science

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Maximum likelihood estimation (MLE)

Maximum likelihood estimation (MLE)

课程: Probability Foundations for Data Science

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Maximum likelihood estimation (MLE)

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- [Instructor] In this chapter, you will explore two estimation techniques. This includes maximum likelihood estimation and maximum a posteriori estimation. Let's begin with maximum likelihood estimation. Maximum likelihood estimation, also known as MLE, is a method used to estimate parameters of a probability distribution. You can estimate parameters by maximum a likelihood function, so that under the assumed model, the observed data is most probable. The point being estimated that maximizes likelihood function is called the maximum likelihood estimate. This measures how like it is that the observed data was generated by the model with those parameter values. Hence the name maximum likelihood estimation. If the likelihood function is differentiable, the derivative test for finding maxima can be applied to solve for the estimated value. In some cases, the first order conditions of the likelihood function can be solved analytically. For example, the ordinary lease squares estimator for…

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