 = mk_dbn(intra, inter, ns, 'discrete', dnodes);
for i=1:4
  bnet.CPD{i} = tabular_CPD(bnet, i);
end
</pre>
<p>
We assume the distributions P(X(t) | X(t-1)) and
P(Y(t) | X(t)) are independent of t for t > 1.
Hence the CPD for nodes 5, 7, ... is the same as for node 3, so we say they
are in the same equivalence class, with node 3 being the "representative"
for this class. In other words, we have tied the parameters for nodes
3, 5, 7, ...
Similarly, nodes 4, 6, 8, ... are tied.
Note, however, that (the parame