Learning causal Bayesian network structures from experimental data

Byron Ellis

doi:10.1198/016214508000000193

Learning causal Bayesian network structures from experimental data

2012-01-01

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DOI：10.1198/016214508000000193

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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.

Keyword:

equi-energy sampling

flow cytometry

Markov chain Monte Carlo

Learning causal Bayesian network structures from experimental data

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