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with pm.Model() as sleep_model:
# Create the alpha and beta parameters
# Assume a normal distribution
alpha = pm.Normal('alpha', mu=0.0, tau=0.05, testval=0.0)
beta = pm.Normal('beta', mu=0.0, tau=0.05, testval=0.0)
# The sleep probability is modeled as a logistic function
p = pm.Deterministic('p', 1. / (1. + tt.exp(beta * time + alpha)))
# Create the bernoulli parameter which uses observed data to inform the algorithm
observed = pm.Bernoulli('obs', p, observed=sleep_obs)
# Using Metropolis Hastings Sampling
step = pm.Metropolis()
# Draw the specified number of samples
sleep_trace = pm.sample(N_SAMPLES, step=step);
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