Given P (l=1|p, k, c), the goal of the M-step is to (re)estimate the model parameters, P (p a |c a , l=1) and P (l=1|k c ), using maximum likelihood estimation.
p (p a |c a , l=1) is estimated as follows: where Count(c a , l=1) is the expected number of times c has attribute value c a when it is the antecedent of p; |a| is the number of possible values of attribute a; θ is the Laplace smoothing parameter, which we set to 1; and Count(p a , c a , l=1) is the expected number of times p has attribute value p a when its antecedent c has attribute value c a .