Learning from mixture of experimental data: a constraint-based approach
We propose a novel approach for learning graphical models when data coming from different experimental conditions are available. We argue that classical constraint-based algorithms can be easily applied to mixture of experimental data given an appropriate conditional independence test. We show that, when perfect statistical inference are assumed, a sound conditional independence test for mixtures of experimental data can consist in evaluating the null hypothesis of conditional independence separately for each experimental condition. We successively indicate how this test can be modified in order to take in account statistical errors. Finally, we provide Proof-of-Concept results for demonstrating the validity of our claims.
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