ov ▁chain ▁Monte ▁Carlo ▁sam pl ers ▁can ▁be ▁interpreted ▁as ▁a ▁way ▁to ▁run ▁in ▁parallel ▁a ▁sequence ▁of ▁Mark ov ▁chain ▁Monte ▁Carlo ▁sam pl ers . ▁For ▁instance , ▁interact ing ▁sim ulated ▁an ne aling ▁algorithms ▁are ▁based ▁on ▁independent ▁Metropol is - H ast ings ▁moves ▁interact ing ▁sequ entially ▁with ▁a ▁selection - res am pling ▁type ▁mechanism . ▁In ▁contrast ▁to ▁traditional ▁Mark ov ▁chain ▁Monte ▁Carlo ▁methods , ▁the ▁precision ▁parameter ▁of ▁this ▁class ▁of ▁interact ing ▁Mark ov ▁chain ▁Monte ▁Carlo ▁sam pl ers ▁is ▁only ▁related ▁to ▁the ▁number ▁of ▁interact ing ▁Mark ov ▁chain ▁Monte ▁Carlo ▁sam pl ers . ▁These ▁advanced ▁particle ▁method ologies ▁belong ▁to ▁the ▁class ▁of ▁Fe yn man - K ac ▁particle ▁models , ▁ ▁also ▁called ▁Se qu ential ▁Monte ▁Carlo ▁or ▁particle ▁filter ▁methods ▁in ▁Bay esian ▁inference ▁and ▁signal ▁processing ▁communities . ▁Inter act ing ▁Mark ov ▁chain ▁Monte ▁Carlo ▁methods ▁can ▁also ▁be ▁interpreted ▁as ▁a ▁mut ation - selection ▁gen etic ▁particle ▁algorithm ▁with ▁Mark ov ▁chain ▁Monte ▁Carlo ▁mut ations . ▁ ▁Mark ov ▁Ch ain ▁quasi - Mon te ▁Carlo ▁( MC Q MC ). ▁ ▁The ▁advantage ▁of ▁low - dis cre p ancy ▁sequences ▁in ▁lieu ▁of ▁random ▁numbers ▁for ▁simple ▁independent ▁Monte ▁Carlo ▁sampling ▁is ▁well ▁known . ▁This ▁procedure , ▁known ▁as ▁Qu asi - Mon te ▁Carlo ▁method ▁( Q MC ), ▁yields ▁an ▁integration ▁error ▁that ▁dec ays ▁at ▁a ▁superior ▁rate ▁to ▁that