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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

Software:

BNT
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