The problem is as follows:

> 
> Simulate flipping three fair coins and counting the number X of heads.
> 
> 
> 1. Use your simulation to estimate P(X = 1) and EX. Compare the estimates with the true values, derived from theoretical computations.
> 2. Modify the above to allow for a biased coin where P(heads)=3/4.
> 
> 
> 

I have been able to simulate an unbiased coin as follows:

```
import random

SIMULATION_COUNT = 9999999

coin_surface_dictionary = {'H':0.5, 'T': 0.5}

def get_coin_surface():
    return random.choice(['H', 'T'])

def get_three_coin_surface():
    list_vector = []
    list_vector.append(get_coin_surface())
    list_vector.append(get_coin_surface())
    list_vector.append(get_coin_surface())
    return list_vector

if __name__ == "__main__":
    one_head_count_int = 0
    for ch in range(1, SIMULATION_COUNT):
        coin_surface_vector = get_three_coin_surface()
        head_count_int = coin_surface_vector.count("H")
        if head_count_int == 1:
            one_head_count_int = one_head_count_int + 1
        # END if
    # END for loop
    probability = one_head_count_int / SIMULATION_COUNT
    print(probability)

```

How can I simulate a biased coin by minimally modifying this source code?