Learning causal Bayesian network structures from experimental data

  • Ellis B
  • Wong W
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Abstract

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.
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.

Author-supplied keywords

  • Equi-energy sampling
  • Flow cytometry
  • Markov chain Monte Carlo

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Authors

  • Byron Ellis

  • Wing Hung Wong

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