Inference in k-TBNs

Inference in a k-TBN computes P(target[t] | observations, interventions) for every declared target at every time slice, under any mix of soft/hard observations and Pearl-style do-interventions – without ever unrolling the model. aGrUM/pyAgrum implements one exact algorithm, a generalization of Murphy’s interface algorithm to order k.