sim=Simulation() sim.add("h'=a",1,plot=True) sim.add_data(t=t,h=h,plot=True) sim.params(a=1) sim.run(0,12) # <markdowncell> # ### Fit the model parameter, $a$ # # Specifying the prior probability distribution for $a$ as uniform between -10 and 10. # <codecell> model=MCMCModel(sim,{'a':[-10,10]}) model.fit(iter=25000) # <markdowncell> # What is the best fit parameter value? # <codecell> model.a # <markdowncell> # ### Rerun the model # <codecell>
sim = Simulation() sim.add("h'=a", 1, plot=True) sim.add_data(t=t, h=h, plot=True) sim.params(a=1) sim.run(0, 12) # <markdowncell> # ### Fit the model parameter, $a$ # # Specifying the prior probability distribution for $a$ as uniform between -10 and 10. # <codecell> model = MCMCModel(sim, {'a': [-10, 10]}) model.fit(iter=25000) # <markdowncell> # What is the best fit parameter value? # <codecell> model.a # <markdowncell> # ### Rerun the model # <codecell>
sim.add_data(t=numOfDays, I=numOfCases, plot=1) sim.run(0, 350) # <codecell> model = MCMCModel( sim, { 'beta0': [0, 1], 'invGamma': [3.5, 10.7], 'beta1': [0, 1], 'q': [0, 100], 'tau': [100, 150], 'invk': [5, 22] }) #model = MCMCModel(sim,{'invGamma':[3.5,10.7],'q':[0,10]}) model.fit(iter=500) model.plot_distributions() # <codecell> Beta0 = model.beta0 # <codecell> Beta1 = model.beta1 # <codecell> #beta = (Beta0 + Beta1)/2 # <codecell>