def LearnMLParams(self, cases):
        """ Learn and set the parameters of the network to the ML estimate
        contained in cases.
       
        Warning: this is destructive, it does not take any prior parameters
                 into account. Assumes that all evidence is specified.
        """
        for v in self.BNet.v.values():
            if v.distribution.isAdjustable:
                v.distribution.initializeAugmentedEq() # sets all a_ij and b_ij to equivalent sample size #momo
        v.distribution.initializeCounts()
        v.distribution.normalize(dim=v.name) # set the initial Pr's to a_ij/(a_ij+b_ij)
       
           
        for case in cases:
            assert(set(case.keys()) == set(self.BNet.v.keys())), "Not all values of 'case' are set"
            for v in self.BNet.v.values():
                if v.distribution.isAdjustable:
                    v.distribution.incrCounts(case)
            v.distribution.setAugmentedAndCounts() #added
           
        for v in self.BNet.v.values():
            if v.distribution.isAdjustable:

        v.distribution.setAugmentedAndCounts() #added
               
        v.distribution.normalize(dim=v.name)