f_season prob(season,[],{'summer': 'winter': }f_sprinkler prob(sprinkler,[season],{'summer':{'on': ,'off': }'winter':{'on': ,'off': }}f_rained prob(rained,[season],{'summer':[ , ]'winter'[ , ]}f_wet prob(grass_wet,[sprinkler,rained]{'on'[[ , ],[ , ]]'off':[[ , ],[ , ]]}f_shiny prob(grass_shiny[grass_wet][[ , ][ , ]]f_shoes prob(shoes_wet[grass_wet][[ , ][ , ]] bn_sprinkler beliefnetwork("pearl' sprinkler example"{seasonsprinklerrainedgrass_wetgrass_shinyshoes_wet}{f_seasonf_sprinklerf_rainedf_wetf_shinyf_shoes} bn_sprinkler_soff beliefnetwork("pearl' sprinkler example (do(sprinkler=off))"{seasonsprinklerrainedgrass_wetgrass_shinyshoes_wet}{f_seasonf_rainedf_wetf_shinyf_shoesprob(sprinkler,[],{'on': ,'off': })}bipartite diagnostic model with noisy-or the belief network bn_no is bipartite diagnostic modelwith independent diseasesand the symtoms depend on the diseaseswhere the cpds are defined using noisy-or bipartite means it is in two partsthe diseases are only connected to the symptoms and the symptoms are only connected to the diseases the output of bn_no show(is shown in figure of this document probgraphicalmodels py -(continued cough variable("cough"boolean( , )fever variable("fever"boolean( , )sneeze variable("sneeze"boolean( , )cold variable("cold",boolean( , )flu variable("flu",boolean( , )covid variable("covid",boolean( , ) p_cold_no prob(cold,[],[ , ]p_flu_no prob(flu,[],[ , ]p_covid_no prob(covid,[],[ , ] p_cough_no noisyor(cough[cold,flu,covid][ ]p_fever_no noisyor(feverflu,covid][ ]p_sneeze_no noisyor(sneeze[cold,flu ][ ] bn_no beliefnetwork("bipartite diagnostic network (noisy-or)"version june