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>
