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