reasoning under uncertainty pearl' sprinkler example season rained sprinkler grass wet grass shiny shoes wet figure the sprinkler belief network f_ta prob(tamper,[],[ , ]f_fi prob(fire,[],[ , ]f_sm prob(smoke,[fire],[[ , ],[ , ]]f_al prob(alarm,[fire,tamper],[[[ ][ ]][[ ][ ]]]f_lv prob(leaving,[alarm],[[ ][ ]]f_re prob(report,[leaving],[[ ][ ]] bn_report beliefnetwork("report-of-leaving"{tamper,fire,smoke,alarm,leaving,report}{f_ta,f_fi,f_sm,f_al,f_lv,f_re}sprinkler example the third belief network is the sprinkler example from pearl the output of bn_sprinkler show(is shown in figure of this document probgraphicalmodels py -(continued season variable("season"["summer","winter"]position=( , )sprinkler variable("sprinkler"["on","off"]position=( , )rained variable("rained"booleanposition=( , )grass_wet variable("grass wet"booleanposition=( , )grass_shiny variable("grass shiny"booleanposition=( , )shoes_wet variable("shoes wet"booleanposition=( , )version june