Maximum likelihood estimation (MLE) is a method that can be used to estimate the parameters of a given distribution.
This tutorial explains how to calculate the MLE for the parameter λ of a Poisson distribution.
Step 1: Write the PDF.
First, write the probability density function of the Poisson distribution:
Step 2: Write the likelihood function.
Next, write the likelihood function. This is simply the product of the PDF for the observed values x1, …, xn.
Step 3: Write the natural log likelihood function.
To simplify the calculations, we can write the natural log likelihood function:
Step 4: Calculate the derivative of the natural log likelihood function with respect to λ.
Next, we can calculate the derivative of the natural log likelihood function with respect to the parameter λ:
Step 5: Set the derivative equal to zero and solve for λ.
Lastly, we set the derivative in the previous step equal to zero and simply solve for λ:
Thus, the MLE turns out to be:
This is equivalent to the sample mean of the n observations in the sample.