reasoning under uncertainty if not rejectedcounts[sample[qvar]+ self display( ,"accepted"tot sum(counts values()as well as the distribution we also include raw counts dist { : /tot if tot> else /len(qvar domainfor ( ,vin counts items()dist["raw_counts"counts return dist likelihood weighting likelihood weighting includes weight for each sample instead of rejecting samples based on observationslikelihood weighting changes the weights of the sample in proportion with the probability of the observation the weight then becomes the probability that the variable would have been rejected probstochsim py -(continued class likelihoodweighting(samplinginferencemethod)"""the class that queries graphical models using likelihood weighting gm is belief network to query ""method_name "likelihood weighting def __init__(selfgm=none)samplinginferencemethod __init__(selfgm def query(self,qvar,obs={},number_samples= ,sample_order=none)"""computes (qvar obswhere qvar is variable obs is {variable:valuedictionary sample_order is list of factors where factors defining the parents come before the factors for the child ""if sample_order is nonesample_order self gm topological_sort(self display( ,*[ for in sample_order if not in obs],sep="\ "counts {val: for val in qvar domainfor in range(number_samples)sample {weight for nvar in sample_orderfac self gm var cpt[nvarif nvar in obssample[nvarobs[nvarweight *fac get_value(sampleelseval sample_one({ :fac get_value({**sample,nvar: }for in nvar domain}version june