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Learning causal Bayesian network structures from experimental data
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DOI：10.1198/016214508000000193.png)
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Abstract
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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.
Keywords:
equi-energy sampling
flow cytometry
Markov chain Monte Carlo
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