On 12.09.2017 13:01, Philipp-Maximilian Jacob wrote:
Hi Tiago,
Thank you for that explanation. A quick follow-up:
If calculating likelihoods of both missing and spurious edges would I expect the output to be on a continuous scale of existence likelihood? Assume there is an edge “c” which I am assuming to be a missing edge and I calculate the likelihood ratios by summing across all three edges (based on `s.get_edges_prob([],[a], entropy_args=dict(partition_dl=False))`, `s.get_edges_prob([],[b], entropy_args=dict(partition_dl=False))` and `s.get_edges_prob([c],[], entropy_args=dict(partition_dl=False))`). If I find \lambda_a > \lambda_c > \lambda_b can I read this that “a” is more likely to be spurious than “c” is to be missing (which in turn is more likely to be spurious than "b" is to be missing)? Or is such a comparison not really meaningful anyways?
Yes, this is totally fine. The "spurious" and "missing" edges are arbitrary modifications to the graph, and the probabilistic model does not distinguish between them. Best, Tiago -- Tiago de Paula Peixoto <tiago@skewed.de>