# name: Gonul Ayci
# e-mail: aycignl@gmail.com
# date: Jan. 2018

def bayesian(A_total, B_total):
    total = A_total + B_total

    prob_A = (A_total * 1.0)/ total
    prob_B = ((B_total) * 1.0)/ total

    # Assume that A and B are independent from each other
    prob_A_and_B = prob_A * prob_B
    prob_A_given_B = prob_A_and_B * 1.0/ prob_B
    prob_B_given_A = (prob_B * prob_A_given_B)/ (prob_A * 1.0)

#
#     Bayes Theorem
#     p(B|A) = p(B, A)/p(A) = (p(A|B)*p(B))/p(A)

#     params: A and B
#     A: model
#     p(A), p(B): prior probabilities
#     p(A|B): likelihood

    print "Posterior probability: ", prob_B_given_A

bayesian(10, 10)

# Result:
# Posterior probability:  0.5