Python Specials #4 Bernoulli Distribution
The Bernoulli distribution models the probability of a binary outcome, where there are only two possible outcomes (often referred to as "success" and "failure"). The distribution is parameterized by a single probability value, p, which represents the probability of success. For example, in the code example, p = 0.3 means that the probability of success is 0.3. The distribution takes on the value 1 (for success) with probability p, and the value 0 (for failure) with probability 1-p.
In the code example, we create a bernoulli object from the scipy.stats module with the given probability of success, and then use its pmf method to calculate the probability of success (getting a 1), and its rvs method to generate a sample of 10 outcomes from the distribution. We also plot the probability mass function (PMF) of the distribution using matplotlib.
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