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Table 4 Hybrid BN structure-learning algorithm
From: Examining the factors influencing microtransit users’ next ride decisions using Bayesian networks
1 | Input a set of variables \(X\) and the empirical ride data \(D\) |
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2 | Identify an expert \(B{N}_{expert}=(X,{A}_{expert})\) based on expert knowledge |
3 | Given \(B{N}_{expert}\) as the structural restrictions, utilize a bootstrap sampling approach from \(D\) to learn \(n\) plausible data-driven BN structures according to the score-based learning algorithms |
4 | Utilize a model averaging approach to select the robust arcs with a probability greater than a statistical significance threshold (0.5 or more). This significance threshold reflects the probability that the selected arcs belong to the true (unknown) structure |
5 | Refine the newly added arcs from Step 4 based on the domain knowledge to obtain a final BN. This step involves checking and adjusting the directions of these newly added arcs so that they are consistent with the causal/dependent effect between the connected nodes |
6 | Perform parameter learning to fit the data with maximum likelihood and obtain the local distributions associated with the nodes of the final BN |