import pymc
theta = pymc.Normal('theta', 0, .88)

X1 = pymc.Bernoulli('X2', p=pymc.Lambda('a', lambda theta=theta:1./(1+np.exp(-(theta-(-0.75))))), value=[1],observed=True)
X2 = pymc.Bernoulli('X3', p=pymc.Lambda('b', lambda theta=theta:1./(1+np.exp(-(theta-0)))), value=[1],observed=True)

model = pymc.Model([theta, X1, X2])
mcmc = pymc.MCMC(model)
mcmc.sample(iter=25000, burn=5000)
trace = (mcmc.trace('theta')[:])
print "nThe MAP value for theta is", trace.sum()/len(trace)

import pymc3

with pymc3.Model() as model:

    theta = pymc3.Normal('theta', 0, 0.88)
    X1 = pymc3.Bernoulli('X1', p=pymc3.Deterministic('b', 1./(1+np.exp(-(theta-(-0.75))))), observed=[1])
    X2 = pymc3.Bernoulli('X2', p=pymc3.Deterministic('c', 1./(1+np.exp(-(theta-(0))))), observed=[1])

    start=pymc3.find_MAP()
    step=pymc3.NUTS(state=start)
    trace = pymc3.sample(20000, step, njobs=1, progressbar=True)

pymc3.traceplot(trace)

AsTensorError: ('Cannot convert <function <lambda> at 0x157323e60> to TensorType', <type 'function'>)