Extending Causal Metamodeling to a non-Markovian Queue 事件

Extending Causal Metamodeling to a non-Markovian Queue arXiv:2606.00795v1 Announce Type: cross Abstract: Metamodels for discrete-event simulations approximate the behavior of simulation models without running expensive simulations. Prior work introduced modular dynamic Bayesian networks (MDBNs) -- a class of metamodels that can estimate a range of probabilistic and causal queries (PCQs) using a single, trained model -- but the method was limited to Markovian systems. In this paper, we initiate