Bayesian Networks in the Absence of Temporal Information - Bayesian Networks in R: With Applications in Systems Biology - page 56

> INT = sapply(1:11, function(x) {
+ which(isachs$INT == x) })
> nodes = names(isachs)[1:11]
> names(INT) = nodes
Given such a list, we can then pass it to tabu as an additional argument for mbde .
In addition, we can combine the use of mbde with model averaging and random
starting points as discussed in Sect. 2.5.1 . To improve the stability of the averaged
network, we generate the set of the starting networks for the tabu searches using the
algorithm from Melan¸on et al. ( 2001 ), which is not limited to connected networks
as the one from Ide and Cozman ( 2002 ). In addition, we actually use only one
generated network every 100 to obtain a more diverse set.
> start = random.graph(nodes = nodes,
+ method = "melancon", num = 500, burn.in = 10ˆ5,
+ every = 100)
> netlist = lapply(start, function(net) {
+ tabu(isachs[, 1:11], score = "mbde", exp = INT,
+ iss = 10, start = net, tabu = 50) })
> arcs = custom.strength(netlist, nodes = nodes)
> bn.mbde = averaged.network(arcs, threshold = 0.85)
As we can see from Fig. 2.10 , bn.mbde is much closer to the validated network
from Sachs et al. ( 2005 ) than any of the other networks learned in this section. All
the arcs from the validated network are correctly learned, even though a few are
reversed. The arcs from bn.mbde that are not present in the validated networks
were identified in the original paper and discarded due to their comparatively low
strength; this may imply that the simulated annealing algorithm used in Sachs et al.
( 2005 ) performs better on this data set than tabu search.
Exercises
2.1. Consider the asia synthetic data set from Lauritzen and Spiegelhalter ( 1988 ),
which describes the diagnosis of a patient at a chest clinic who has just come back
from a trip to Asia and is showing dyspnea.
(a) Load the data set from the bnlearn package and investigate its characteristics
using the exploratory analysis techniques covered in Chap. 1 .
(b) Create a bn object with the network structure described in the manual page of
asia .
(c) Derive the skeleton, the moral graph, and the CPDAG representing the equiva-
lence class of the network. Plot them using graphviz.plot .
(d) Identify the parents, the children, the neighbors, and the Markov blanket of each
node.