source('my.cn.inference.r')
cpt2.x1 = matrix(c(1, 0), ncol = 2,
dimnames = list(NULL, c('true', 'false')))
# In this part of the talk, we use a very simplistic
# representation of binary credal networks:
#
# Two BNs, each one gives one of the vertices of
# each local credal set
net.1 = custom.fit(net, dist = list(x1=cpt1.x1,
x2=cpt1.x2, x3=cpt1.x3, x4=cpt1.x4))
net.2 = custom.fit(net, dist = list(x1=cpt2.x1,
x2=cpt1.x2, x3=cpt1.x3, x4=cpt1.x4))
# So net.2 is precise apart from variable x1, which
# has 0.7 <= P(x1) <= 1