Patent ID: 11068799

Abstract:
Systems and method for perturbing a system include obtaining directed acyclic/cyclic graph candidates {GI, . . . , GN} for the system. Each Gi in {Gj, . . . GN} includes a causal relationship between a parent and child node. {GI, GN} demonstrate Markov equivalence. Observed data D is obtained for the nodes. For each respective Gi, the marginal probability of a parent node xi in Gi is clamped by D while computing a distribution of marginal probabilities for a child node yi, by Bayesian network or Dynamic Bayesian network belief propagation using an interaction function. The observed distribution for the child node yi, in D and the computed distribution of marginal probabilities for the child node yi are scored using a nonparametric function, and such scores inform the selection of a directed/cyclic graph from {GI, . . . , GN}. The system is perturbed using a perturbation that relies upon a causal relationship in the selected directed acyclic/cyclic graph.