import numpy as np
# %% Number of trials:
num_trials = 100000
# %% Params of np.random.choice():
sample_space = np.array([1, 2, 3, 4, 5, 6])
sample_size = 1
replace = True
prob = [1/6, 1/6, 1/6, 1/6, 1/6, 1/6]
# %% Initializing counter of event A:
count_A = 0
# %% For loop simulating the n events:
for k in range(0, num_trials):
# Sample number k (event number k):
sample_k = np.random.choice(
sample_space,
sample_size,
replace,
prob)
# Updating counter if event A has occured:
if ((5 in sample_k) or (6 in sample_k)): count_A += 1
# %% Calculating and printing probability estimate:
prob_estimate_A = count_A/num_trials
print('Estimate of prob of event A =', prob_estimate_A)