Information criterion for approximation of unnormalized densities.

ABSTRACT: This paper considers the problem of approximating an unknown density when it can be evaluated up to a normalizing constant at a finite number of points. This density approximation problem is ubiquitous in statistics, such as approximating a posterior density for Bayesian inference and estimating an optimal density for importance sampling. We consider a parametric approximation approach and cast it as a model selection problem to find the best model in pre-specified distribution families (e.g., select the best number of Gaussian mixture components and their parameters). This problem cannot be addressed with traditional approaches that maximize the (marginal) likelihood of a model, for example, using the Akaike information criterion (AIC) or Bayesian information criterion (BIC). We instead a

SUBMITTER: Choe JY 

PROVIDER: S-EPMC11913297 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature