▁however , ▁is ▁sub sum ed ▁by , ▁but ▁relax ed ▁in ▁D S ▁theory : ▁ ▁For ▁example , ▁a ▁Bay esian ▁would ▁model ▁the ▁color ▁of ▁a ▁car ▁as ▁a ▁probability ▁distribution ▁over ▁( red , ▁green , ▁blue ), ▁assigning ▁one ▁number ▁to ▁each ▁color . ▁▁ ▁D emp ster – S ha fer ▁would ▁assign ▁numbers ▁to ▁each ▁of ▁( red , ▁green , ▁blue , ▁( red ▁or ▁green ), ▁( red ▁or ▁blue ), ▁( green ▁or ▁blue ), ▁( red ▁or ▁green ▁or ▁blue )) ▁which ▁do ▁not ▁have ▁to ▁coh ere , ▁for ▁example ▁Bel ( red )+ Bel ( green ) ▁!= ▁Bel ( red ▁or ▁green ). ▁▁▁ ▁This ▁may ▁be ▁computation ally ▁more ▁efficient ▁if ▁a ▁witness ▁reports ▁" I ▁saw ▁that ▁the ▁car ▁was ▁either ▁blue ▁or ▁green " ▁in ▁which ▁case ▁the ▁belief ▁can ▁be ▁assigned ▁in ▁a ▁single ▁step ▁rather ▁than ▁breaking ▁down ▁into ▁values ▁for ▁two ▁separate ▁colors . ▁ ▁However ▁this ▁can ▁lead ▁to ▁ir r ational ▁conclus ions . ▁ ▁Equ ival ently , ▁each ▁of ▁the ▁following ▁conditions ▁defines ▁the ▁Bay esian ▁special ▁case ▁of ▁the ▁D S ▁theory : ▁▁▁ ▁For ▁finite ▁X , ▁all ▁foc al ▁elements ▁of ▁the ▁belief ▁function ▁are ▁sing let ons . ▁ ▁Bay es ' ▁conditional ▁probability ▁is ▁a ▁special ▁case ▁of ▁D emp ster ' s ▁rule ▁of ▁combination . ▁ ▁It ▁has ▁been ▁argued ▁that ▁D S ▁theory ▁provides ▁a ▁clearer ▁distinction ▁between ▁ep ist em