I am trying to implement the censored data example in Lee&Wagenmakers' book (Chapter 5.5, page 70). In pymc2, I have the following model:
nattempts = 950
nfails = 949
n = 50 # Number of questions
y = np.zeros(nattempts)
y[nattempts-1] = 1
z = 30
unobsmin = 15
unobsmax = 25
unobsrange = np.arange(unobsmin,unobsmax+1)
theta = pymc.Uniform("theta",lower = .25, upper = 1)
@pymc.observed
def Ylike(value=z, theta = theta, n=n, censorn=nfails, unobs=unobsrange):
ylikeobs = pymc.binomial_like(x=value, n=n, p=theta)
ylikeunobs = np.array([])
for i in unobs:
ylikeunobs = np.append(pymc.binomial_like(x=i, n=n, p=theta),ylikeunobs)
return ylikeobs+sum(ylikeunobs)*censorn
testmodel = pymc.Model([theta,Ylike])
mcmc = pymc.MCMC(testmodel)
mcmc.sample(iter = 20000, burn = 50, thin = 2)
which involved the decorater @pymc.observed
.
I think I need to express the likelihood using the pm.DensityDist
, however, I could not figure it out how to.