plot_stats(bn_reportp,tamper,true,{report:true,smoke:true},number_samples= number_runs= plot_stats(bn_reportr,tamper,true,{report:true,smoke:true},number_samples= number_runs= plot_stats(bn_reportl,tamper,true,{report:true,smoke:true},number_samples= number_runs= plot_stats(bn_reportg,tamper,true,{report:true,smoke:true},number_samples= number_runs= def plot_mult(methodsexampleqvarqvalobsnumber_samples= number_runs= )for method in methodssolver method(exampleif isinstance(method,samplinginferencemethod)plot_stats(solverqvarqvalobsnumber_samplesnumber_runselseplot_stats(solverqvarqvalobsnumber_runs from probrc import probrc try following (but it takes while methods [probrc,rejectionsampling,likelihoodweighting,particlefiltering,gibbssampling#plot_mult(methods,bn_report,tamper,true,{report:true,smoke:false},number_samples= number_runs= plot_mult(methods,bn_report,tamper,true,{report:false,smoke:true},number_samples= number_runs= sprinkler exampleplot_stats(bn_sprinklerr,shoes_wet,true,{grass_shiny:true,rained:true},number_samples= plot_stats(bn_sprinklerl,shoes_wet,true,{grass_shiny:true,rained:true},number_samples= hidden markov models this code for hidden markov models is independent of the graphical models codeto keep it simple section gives code that models hidden markov modelsand more generallydynamic belief networksusing the graphical models code this hmm code assumes there are multiple boolean observation variables that depend on the current state and are independent of each other given the state probhmm py -hidden markov model version june