Comprehensive review of Bayesian network applications in gastrointestinal cancers

Figure 1 A typical structure of a Bayesian network. It consists of a directed acyclic graph and the corresponding conditional probability tables. If node C is selected as the target node (yellow node), then the green nodes (B, F, D, H, G) form the Markov blanket of node C, where B and F are parent nodes, D and H are child nodes, and G is a spouse node.

Figure 2 Classification of algorithms related to Bayesian network structure learning. DAG: Directed acyclic graph; PC: Peter and Clark; ACOG: Ancestral constrained order graph; BDeu: Bayesian Drichlet equivalent uniform; BIC: Bayesian information criterion; MDL: Minimum description length; GS: Greedy search; OBS: Ordering-based search; BiHS: Bidirectional heuristic search; ILP: Integer linear programming; MMHC: Max-Min Hill-Climbing; MMPC: Max-Min Parent and Children; DP: Dynamic programming; AWA*: Anytime Window A* algorithm; MCMC: Markov Chain Monte Carlo; SEM: Structural expectation-maximization.