terminal and non-terminal node
Technology: Information Technology > Artificial Intelligence > Machine Learning > Learning Graphical Models > Directed Networks > Bayesian Learning (0.93)
1. [ALL] As R3 appreciates, our paper is mainly theoretical in nature and the focus has been to present a correct
Regarding "plots are noisy and don't really support well the claim that the algorithm recovers the true Check the sharp jump in Figure 2 which is expected based on Theorem 3. Similarly, Figure 3 shows that Markov blanket can be recovered with sufficient number of observational data. NP-hard [Chickering, 1996, Learning Bayesian Networks Is NP-Complete]. Rank-2 is only used for clarity. Reviewer 2 has asked to present a case where Assumption 4 is violated. Assume that every variable can take 4 values.