reasoning under uncertainty bipartite diagnostic network (noisy-orcold flu covid cough fever sneeze figure bipartite diagnostic network {coughfeversneezecoldflucovid}{p_cold_nop_flu_nop_covid_nop_cough_nop_fever_nop_sneeze_no} to see the conditional probability of noisy-or doprint(p_cough_no to_table() example from box "noisy-or compared to logistic regressionx variable(" ",booleanw print(noisyor( ,[ , , , ],[ -()/( - ) -()/( - ) -()/( - ) -()/( - )]to_table(given={ :true})bipartite diagnostic model with logistic regression the belief network bn_lr is bipartite diagnostic modelwith independent diseasesand the symtoms depend on the diseaseswhere the cpds are defined using logistic regression it has the same graphical structure as the previous example (see figure this has the (approximatelythe same conditional probabilities as the previous example when zero or one diseases are present note that sigmoid(- probgraphicalmodels py -(continuedversion june