▁belief ▁and ▁pla us ibility ▁functions ▁but ▁no ▁well - defined ▁mass ▁function . ▁ ▁D emp ster ' s ▁rule ▁of ▁combination ▁The ▁problem ▁we ▁now ▁face ▁is ▁how ▁to ▁combine ▁two ▁independent ▁sets ▁of ▁probability ▁mass ▁assign ments ▁in ▁specific ▁situations . ▁In ▁case ▁different ▁sources ▁express ▁their ▁belief s ▁over ▁the ▁frame ▁in ▁terms ▁of ▁belief ▁constraints ▁ ▁such ▁as ▁in ▁case ▁of ▁giving ▁hints ▁or ▁in ▁case ▁of ▁express ing ▁prefer ences , ▁then ▁D emp ster ' s ▁rule ▁of ▁combination ▁is ▁the ▁appropriate ▁fusion ▁operator . ▁This ▁rule ▁der ives ▁common ▁shared ▁belief ▁between ▁multiple ▁sources ▁and ▁ign ores ▁all ▁the ▁conflic ting ▁( non - shared ) ▁belief ▁through ▁a ▁normal ization ▁factor . ▁Use ▁of ▁that ▁rule ▁in ▁other ▁situations ▁than ▁that ▁of ▁combining ▁belief ▁constraints ▁has ▁come ▁under ▁serious ▁criticism , ▁such ▁as ▁ ▁in ▁case ▁of ▁f using ▁separate ▁belief s ▁estimates ▁from ▁multiple ▁sources ▁that ▁are ▁to ▁be ▁integrated ▁in ▁a ▁cum ulative ▁manner , ▁and ▁not ▁as ▁constraints . ▁C um ulative ▁fusion ▁means ▁that ▁all ▁probability ▁masses ▁from ▁the ▁different ▁sources ▁are ▁reflected ▁in ▁the ▁derived ▁belief , ▁so ▁no ▁probability ▁mass ▁is ▁ignored . ▁ ▁Specifically , ▁the ▁combination ▁( called ▁the ▁joint ▁mass ) ▁is ▁calculated ▁from ▁the ▁two ▁sets ▁of ▁masses ▁m 1 ▁and ▁m 2 ▁in ▁the ▁following ▁manner : ▁ ▁where ▁ ▁K ▁is ▁a ▁measure ▁of ▁the ▁amount ▁of ▁conflict ▁between ▁the ▁two ▁mass ▁sets . ▁ ▁Effect s ▁of ▁conflict ▁The ▁normal ization