Ellis, Byron; Wong, Wing Hung Learning causal Bayesian network structures from experimental data. (English) Zbl 1471.62056 J. Am. Stat. Assoc. 103, No. 482, 778-789 (2008). 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. Cited in 26 Documents 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 Keywords:equi-energy sampling; flow cytometry; Markov chain Monte Carlo Software:BNT PDFBibTeX XMLCite \textit{B. Ellis} and \textit{W. H. Wong}, J. Am. Stat. Assoc. 103, No. 482, 778--789 (2008; Zbl 1471.62056) Full Text: DOI