Pattern Recognition and Machine Learning

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Exercises 421

8.16 ( ) Consider the inference problem of evaluatingp(xn|xN)for the graph shown
in Figure 8.38, for all nodesn∈{ 1 ,...,N− 1 }. Show that the message passing
algorithm discussed in Section 8.4.1 can be used to solve this efficiently, and discuss
which messages are modified and in what way.


8.17 ( ) Consider a graph of the form shown in Figure 8.38 havingN=5nodes, in
which nodesx 3 andx 5 are observed. Use d-separation to show thatx 2 ⊥⊥x 5 |x 3.
Show that if the message passing algorithm of Section 8.4.1 is applied to the evalu-
ation ofp(x 2 |x 3 ,x 5 ), the result will be independent of the value ofx 5.


8.18 ( ) www Show that a distribution represented by a directed tree can trivially
be written as an equivalent distribution over the corresponding undirected tree. Also
show that a distribution expressed as an undirected tree can, by suitable normaliza-
tion of the clique potentials, be written as a directed tree. Calculate the number of
distinct directed trees that can be constructed from a given undirected tree.


8.19 ( ) Apply the sum-product algorithm derived in Section 8.4.4 to the chain-of-
nodes model discussed in Section 8.4.1 and show that the results (8.54), (8.55), and
(8.57) are recovered as a special case.


8.20 ( ) www Consider the message passing protocol for the sum-product algorithm on
a tree-structured factor graph in which messages are first propagated from the leaves
to an arbitrarily chosen root node and then from the root node out to the leaves. Use
proof by induction to show that the messages can be passed in such an order that
at every step, each node that must send a message has received all of the incoming
messages necessary to construct its outgoing messages.


8.21 ( ) www Show that the marginal distributionsp(xs)over the sets of variables
xsassociated with each of the factorsfx(xs)in a factor graph can be found by first
running the sum-product message passing algorithm and then evaluating the required
marginals using (8.72).


8.22 ( ) Consider a tree-structured factor graph, in which a given subset of the variable
nodes form a connected subgraph (i.e., any variable node of the subset is connected
to at least one of the other variable nodes via a single factor node). Show how the
sum-product algorithm can be used to compute the marginal distribution over that
subset.


8.23 ( ) www In Section 8.4.4, we showed that the marginal distributionp(xi)for a
variable nodexiin a factor graph is given by the product of the messages arriving at
this node from neighbouring factor nodes in the form (8.63). Show that the marginal
p(xi)can also be written as the product of the incoming message along any one of
the links with the outgoing message along the same link.


8.24 ( ) Show that the marginal distribution for the variablesxsin a factorfs(xs)in
a tree-structured factor graph, after running the sum-product message passing algo-
rithm, can be written as the product of the message arriving at the factor node along
all its links, times the local factorf(xs), in the form (8.72).

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