▁Lap laces ' ▁principle ▁of ▁ins u fficient ▁reason ▁( ass ume ▁all ▁out comes ▁equally ▁likely ); ▁these ▁may ▁be ▁considered ▁alternative ▁solutions ▁to ▁the ▁problem ▁info - g ap ▁addresses . ▁ ▁Further , ▁as ▁discussed ▁at ▁decision ▁theory : ▁alternatives ▁to ▁probability ▁theory , ▁probabil ists , ▁particularly ▁Bay es ians ▁probabil ists , ▁argue ▁that ▁optimal ▁decision ▁rules ▁( form ally , ▁adm iss ible ▁decision ▁rules ) ▁can ▁always ▁be ▁derived ▁by ▁probabil istic ▁methods ▁( this ▁is ▁the ▁statement ▁of ▁the ▁complete ▁class ▁the or ems ), ▁and ▁thus ▁that ▁non - prob abil istic ▁methods ▁such ▁as ▁info - g ap ▁are ▁unnecessary ▁and ▁do ▁not ▁yield ▁new ▁or ▁better ▁decision ▁rules . ▁ ▁Max imin ▁ ▁As ▁att ested ▁by ▁the ▁rich ▁literature ▁on ▁robust ▁optimization , ▁maxim in ▁provides ▁a ▁wide ▁range ▁of ▁methods ▁for ▁decision ▁making ▁in ▁the ▁face ▁of ▁severe ▁uncertainty . ▁ ▁Indeed , ▁as ▁discussed ▁in ▁criticism ▁of ▁info - g ap ▁decision ▁theory , ▁info - g ap ' s ▁robust ness ▁model ▁can ▁be ▁interpreted ▁as ▁an ▁instance ▁of ▁the ▁general ▁maxim in ▁model . ▁ ▁Bay esian ▁analysis ▁ ▁As ▁for ▁Lap laces ' ▁principle ▁of ▁ins u fficient ▁reason , ▁in ▁this ▁context ▁it ▁is ▁convenient ▁to ▁view ▁it ▁as ▁an ▁instance ▁of ▁Bay esian ▁analysis . ▁ ▁The ▁ess ence ▁of ▁the ▁Bay esian ▁analysis ▁is ▁applying ▁prob abilities ▁for ▁different ▁possible ▁realiz ations ▁of ▁the ▁uncertain ▁parameters . ▁In ▁the ▁case ▁of ▁Knight ian