Created
June 3, 2015 01:19
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import pymc3 as pm | |
import numpy as np | |
# 1-year-old children in Jordan | |
kids = np.array([180489, 191817, 190830]) | |
# Proportion of population in Amman | |
amman_prop = 0.35 | |
# infant RSV cases in Al Bashir hostpital | |
rsv_cases = np.array([40, 59, 65]) | |
with pm.Model() as rsv_model: | |
# Al Bashir hospital market share | |
market_share = pm.Uniform('market_share', 0.5, 0.6) | |
# Number of 1 y.o. in Amman | |
n_amman = pm.Binomial('n_amman', kids, amman_prop, shape=3) | |
# Prior probability | |
prev_rsv = pm.Beta('prev_rsv', 1, 5, shape=3) | |
# RSV in Amman | |
y_amman = pm.Binomial('y_amman', n_amman, prev_rsv, shape=3) | |
# Likelihood for number with RSV in hospital (assumes Pr(hosp | RSV) = 1) | |
y_hosp = pm.Binomial('y_hosp', y_amman, market_share, observed=rsv_cases) | |
step1 = pm.NUTS(vars=[market_share, prev_rsv]) | |
step2 = pm.Metropolis(vars=[n_amman, y_amman]) | |
trace = pm.sample(20000, step=(step1, step2)) |
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