Bayesian model reduction

Understand

This paper reviews recent developments in statistical structure learning; namely, Bayesian model reduction.

• Bayesian model reduction is a method for rapidly computing the evidence and parameters of probabilistic models that differ only in their priors.
• In the setting of variational Bayes this has an analytical solution, which finesses the problem of scoring large model spaces in model comparison or structure learning.
• In this technical note, we review Bayesian model reduction and provide the relevant equations for several discrete and continuous probability distributions.