coin_model.py

import random

# A stochastic model with a single input `p` and a single output `side`
# `p` is the probability of throwing tail
# `side` is either 'head' or 'tail'
def coin_model(p, seed=None):
    # The seed allows us to control randomness and ensure reproducibility
    random.seed(seed)

    # Mathematically, `side_output` is a discrete random variable
    # with a Bernoulli distribution B(p) (1 is 'tail' and 0 is 'head')
    side_output = lambda: 'tail' if random.random() < p else 'head'
    return side_output

if __name__ == '__main__':
    coin = coin_model(0.7, seed=123)
    runs = 5
    for _ in range(runs):
        print(coin())