Comprehensive review of Bayesian network applications in gastrointestinal cancers

Table 2 Some methodologies of Bayesian network inference

Algorithm	Network type	Complexity	Accuracy	Advantages	Ref.
Variable elimination	Single, multi-connected networks	Exponential in the number of variables in factorization	Exact	Simple, easy to use	[22]
Junction tree	Single, multi-connected networks	Exponential in the size of the largest clique	Exact	Fastest method, suitable for sparse networks	[22]
Differential method	Single, multi-connected networks	Proportional to the complexity of differentiation operations	Exact	Can solve multiple problems simultaneously	[23]
Stochastic sampling	Single, multi-connected networks	Inversely proportional to the probability of evidence variables	Approximate	Simple, widely applicable, and generally effective	[24]
Loopy belief propagation	Single, multi-connected networks	Exponential in the number of loops in the network	Approximate	Performs well when the algorithm converges	[25]