Figure 3.

Probabilistic Model. The probabilistic graphical model represents conditional independence relationships between the data <a onClick="popup('http://www.biomedcentral.com/1471-2164/13/S5/S2/mathml/M58','MathML',630,470);return false;" target="_blank" href="http://www.biomedcentral.com/1471-2164/13/S5/S2/mathml/M58">View MathML</a>, the network structure <a onClick="popup('http://www.biomedcentral.com/1471-2164/13/S5/S2/mathml/M5','MathML',630,470);return false;" target="_blank" href="http://www.biomedcentral.com/1471-2164/13/S5/S2/mathml/M5">View MathML</a>, and the hyper-parameter of the prior on <a onClick="popup('http://www.biomedcentral.com/1471-2164/13/S5/S2/mathml/M5','MathML',630,470);return false;" target="_blank" href="http://www.biomedcentral.com/1471-2164/13/S5/S2/mathml/M5">View MathML</a>. The conditional independence relationships can be obtained from the graph according to the standard rules of factorization in Bayesian networks, as discussed, e.g., in [34]. This leads to the following expansion: <a onClick="popup('http://www.biomedcentral.com/1471-2164/13/S5/S2/mathml/M59','MathML',630,470);return false;" target="_blank" href="http://www.biomedcentral.com/1471-2164/13/S5/S2/mathml/M59">View MathML</a>.

Werhli BMC Genomics 2012 13(Suppl 5):S2   doi:10.1186/1471-2164-13-S5-S2