Second, common entropy can be used to improve constraint-based methods such as PC or FCI algorithms in the small-sample regime,wherethesemethods areknowntostruggle.

PSD studied probability have14] Notation.

Marginal likelihood (ML), sometimes calledevidence, is a central quantity in Bayesian learning as it measures how well a model can describe a particular dataset.

Marginal likelihood (ML), sometimes calledevidence, is a central quantity in Bayesian learning as it measures how well a model can describe a particular dataset.

But it can also be problematic since it can induce inconsistencies in, e.g., Hessian-based flatness measures,