Question

In unbiased coin flip H or T occurs 50% of times.

But I want to simulate coin which gives H with probability ‘p’ and T with probability ‘(1-p)’.

something like this:

def flip(p):    '''this function return H with probability p'''    # do something    return result  >> [flip(0.8) for i in xrange(10)] [H,H,T,H,H,H,T,H,H,H] 

Answer 1

random.random() returns a uniformly distributed pseudo-random floating point number in the range [0, 1). This number is less than a given number p in the range [0,1) with probability p. Thus:

def flip(p):     return 'H' if random.random() < p else 'T' 

Some experiments:

>>> N = 100 >>> flips = [flip(0.2) for i in xrange(N)] >>> float(flips.count('H'))/N 0.17999999999999999  # Approximately 20% of the coins are heads  >>> N = 10000 >>> flips = [flip(0.2) for i in xrange(N)] >>> float(flips.count('H'))/N 0.20549999999999999  # Better approximation