Biology Reference
In-Depth Information
•
arcs
: the arcs present in the network, in the same two-column format used in
the call to
arcs
above.
All these information can be accessed through ad hoc accessor functions, some of
which will be illustrated in this section. Furthermore, a synthetic view of the network
is provided by the
print
method for this class.
>ug
Random/Generated Bayesian network
model:
[undirected graph]
nodes:
5
arcs:
6
undirected arcs:
6
directed arcs:
0
average markov blanket size:
2.40
average neighbourhood size:
2.40
average branching factor:
0.00
generation algorithm: Empty
The structure of
ug
is shown in Fig.
2.2
, along with one of the Bayesian networks
that will be learned from
marks
in Sect.
2.3.4
and the corresponding equivalence
class. As before, we can create a
bn
object for that Bayesian network with:
> dag = empty.graph(names(marks))
> arcs(dag) = matrix(
+
c("VECT", "MECH", "ALG", "MECH", "ALG", "VECT",
+
"ANL", "ALG", "STAT", "ALG", "STAT", "ANL"),
+
ncol = 2, byrow = TRUE,
+
dimnames = list(c(), c("from", "to")))
> dag
Random/Generated Bayesian network
model:
[STAT][ANL|STAT][ALG|ANL:STAT][VECT|ALG]
[MECH|VECT:ALG]
nodes:
5
arcs:
6
undirected arcs:
0
directed arcs:
6
average markov blanket size:
2.40
average neighbourhood size:
2.40
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