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Bernal S D_2010.pdf - University of Plymouth

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3 J. DEFINITION AND MATHEMATICAL FORMULATION<br />

its descendants is available. This rule tells us that if nothing is known about a common<br />

effect <strong>of</strong> two (or more) causes, then the cau.ses are independent. In other words Gales is<br />

not an indicator <strong>of</strong> Moon, and vice versa. However, as soon as some evidence is available<br />

on a common effect, the causes become dependent. If, for example, we receive some<br />

information on the state <strong>of</strong> VVijvcv. then Gales and Moon become compeiing explanations<br />

for this effect. Thus, receiving infonnation about one <strong>of</strong> the causes either confirm.s or<br />

dismisses the other one as the cause <strong>of</strong> Waves. Note that even if the initial information<br />

about the Waves is not reliable (s<strong>of</strong>t evidence), Gales and Moon still become dependent.<br />

The property <strong>of</strong> converging connections, where information about the state <strong>of</strong> a parent<br />

node provides an explanation for an observed effect, and hence contirms or dismisses<br />

another parent node as the cause <strong>of</strong> the effect, is <strong>of</strong>ten referred to as the explaining away<br />

effect or as inlercausal inference. I-or example, knowing Gales are present strongly sug­<br />

gests these are responsible for the Waves, hence explaining away the Moon as die cause<br />

<strong>of</strong> the Waves.<br />

Critically, one <strong>of</strong> the fundamental properties <strong>of</strong> directed acyclic graphs (Bayesian networks) is<br />

that for a given node X. the set <strong>of</strong> its parents, rijf, d-separales this node from all other subsets<br />

Y with no descendants <strong>of</strong> X, such thatX ±L f | Ilx. In other words, each node in the network is<br />

conditionally independent from its non-descendants, given its parents. This allows us to obtain<br />

the factorization <strong>of</strong> the joint probabiHly distribution shown in h^^uation (3.9), as the following<br />

property is satisfied;<br />

3.3.2.3 Cycles and acyclic graphs<br />

p{x,\nx„Y) = p{x^nx^ (3.11)<br />

A chain consists <strong>of</strong> a series <strong>of</strong> nixies where each successive node in the chain is connected to<br />

the previous one by an edge. A path is a chain where each connection edge in the chain has the<br />

same directionality, i.e. all are serial connections. For example, nodes M —> W -^ 5 (l-igure 3.2<br />

form a path; while nodes M ^W

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