p_cold_lr prob(cold,[],[ , ]p_flu_lr prob(flu,[],[ , ]p_covid_lr prob(covid,[],[ , ] p_cough_lr logisticregression(cough[cold,flu,covid][- ]p_fever_lr logisticregression(feverflu,covid][- ]p_sneeze_lr logisticregression(sneeze[cold,flu ][- ] bn_lr beliefnetwork("bipartite diagnostic network logistic regression"{coughfeversneezecoldflucovid}{p_cold_lrp_flu_lrp_covid_lrp_cough_lrp_fever_lrp_sneeze_lr} to see the conditional probability of noisy-or do#print(p_cough_lr to_table() example from box "noisy-or compared to logistic regressionfrom learnlinear import sigmoidlogit =logit( variable(" ",booleanprint(logisticregression( ,[ , , , ],[ logit( )- logit( )- logit( )- logit( )- ]to_table(given={ :true})try to predict what would happen (and then testif we had =logit( inference methods each of the inference methods implements the query method that computes the posterior probability of variable given dictionary of {variable valueobservations the methods are displayable because they implement the display method which is currently text-based probgraphicalmodels py -(continued from display import displayable class inferencemethod(displayable)"""the abstract class of graphical model inference methods""method_name "unnamedeach method should have method name def __init__(self,gm=none)self gm gm def query(selfqvarobs={})"""returns {value:probdictionary for the query variable""version june