▁combining ▁evidence , ▁often ▁with ▁a ▁view ▁to ▁handling ▁conflicts ▁in ▁evidence ▁better . ▁The ▁early ▁contributions ▁have ▁also ▁been ▁the ▁starting ▁points ▁of ▁many ▁important ▁develop ments , ▁including ▁the ▁transfer able ▁belief ▁model ▁and ▁the ▁theory ▁of ▁hints . ▁ ▁Over view ▁D emp ster – S ha fer ▁theory ▁is ▁a ▁general ization ▁of ▁the ▁Bay esian ▁theory ▁of ▁subject ive ▁probability . ▁Bel ief ▁functions ▁base ▁degrees ▁of ▁belief ▁( or ▁confidence , ▁or ▁trust ) ▁for ▁one ▁question ▁on ▁the ▁prob abilities ▁for ▁a ▁related ▁question . ▁The ▁degrees ▁of ▁belief ▁themselves ▁may ▁or ▁may ▁not ▁have ▁the ▁mathematical ▁properties ▁of ▁prob abilities ; ▁how ▁much ▁they ▁differ ▁depends ▁on ▁how ▁closely ▁the ▁two ▁questions ▁are ▁related . ▁Put ▁another ▁way , ▁it ▁is ▁a ▁way ▁of ▁representing ▁ep ist em ic ▁pla us ib ilities ▁but ▁it ▁can ▁yield ▁answers ▁that ▁contradict ▁those ▁arrived ▁at ▁using ▁probability ▁theory . ▁ ▁O ften ▁used ▁as ▁a ▁method ▁of ▁sensor ▁fusion , ▁D emp ster – S ha fer ▁theory ▁is ▁based ▁on ▁two ▁ideas : ▁obtain ing ▁degrees ▁of ▁belief ▁for ▁one ▁question ▁from ▁subject ive ▁prob abilities ▁for ▁a ▁related ▁question , ▁and ▁D emp ster ' s ▁rule ▁for ▁combining ▁such ▁degrees ▁of ▁belief ▁when ▁they ▁are ▁based ▁on ▁independent ▁items ▁of ▁evidence . ▁In ▁ess ence , ▁the ▁degree ▁of ▁belief ▁in ▁a ▁proposition ▁depends ▁primarily ▁upon ▁the ▁number ▁of ▁answers ▁( to ▁the ▁related ▁questions ) ▁containing ▁the ▁proposition , ▁and ▁the ▁subject ive