Learning causal Bayesian network structures from experimental data. (English) Zbl 1471.62056
Summary: We propose a method for the computational inference of directed acyclic graphical structures given data from experimental interventions. Order-space Markov chain Monte Carlo, equi-energy sampling, importance weighting, and stream-based computation are combined to create a fast algorithm for learning causal Bayesian network structures.
MSC:
62-08 | Computational methods for problems pertaining to statistics |
62F15 | Bayesian inference |
68T35 | Theory of languages and software systems (knowledge-based systems, expert systems, etc.) for artificial intelligence |