Poisson random variable
The numpy.random module also has a number of useful probability distributions for both discrete and continuous random variables. In this exercise, you will learn how to draw samples from a probability distribution.
In particular, you will draw samples from a very important discrete probability distribution, the Poisson distribution, which is typically used for modeling the average rate at which events occur.
Following the exercise, you should be able to apply these steps to any of the probability distributions found in numpy.random. In addition, you will also see how the sample mean changes as we draw more samples from a distribution.
This exercise is part of the course
Statistical Simulation in Python
Exercise instructions
- Using
np.random.poisson()draw samples from a Poisson distribution usinglam(lambda) andsize_1. - Repeat the above step, but this time use
size_2. - For each of the above samples, calculate the absolute difference between their mean and lambda using
np.mean()andabs().
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Initialize seed and parameters
np.random.seed(123)
lam, size_1, size_2 = 5, 3, 1000
# Draw samples & calculate absolute difference between lambda and sample mean
samples_1 = np.random.poisson(____, ____)
samples_2 = np.random.poisson(____, ____)
answer_1 = abs(____)
answer_2 = abs(____)
print("|Lambda - sample mean| with {} samples is {} and with {} samples is {}. ".format(size_1, answer_1, size_2, answer_2))