import csv
import pymc3 as pc3
import numpy as np
import pandas as pd
PLA = pd.read_csv('./productliability_award.csv')
TH_MODEL = pc3.Model()
with TH_MODEL:
A = pc3.Normal('alpha', mu=0, sd=10)
BJR = pc3.Normal('betaJR', mu=0, sd=10)
BL = pc3.Normal('betaL', mu=0, sd=10)
BN = pc3.Normal('betaN', mu=0, sd=10)
BO = pc3.Normal('betaO', mu=0, sd=10)
BLO = pc3.Normal('betaLO', mu=0, sd=10)
BNO = pc3.Normal('betaNO', mu=0, sd=10)
P = A + BL * PLA['liability'] + BN * PLA['negligence'] + BO\
* PLA['oralArg'] + BLO * PLA['liability'] * PLA['oralArg'] + BNO\
* PLA['negligence'] * PLA['oralArg']
AWARD = pc3.Binomial('award', 1, P)
MAP_PLA0 = pc3.find_MAP(model=TH_MODEL)
print(MAP_PLA0)
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