2016/11/30 by Meng Li, David B. Dunson
Computer Science · Mathematics · #Bayesian Modeling and Causal Inference #Bayesian probability #Gaussian Processes and Bayesian Inference #Gaussian process #Imperfect #Model selection #Nonparametric statistics #Parametric statistics #Selection (genetic algorithm) #Sensitivity (control systems) #Statistical Methods and Bayesian Inference #Weighting #stat.ME
paper · pdf · doi:10.1080/01621459.2019.1611140
arxiv created 2018/06/01 · openalex created_date 2018/06/13 · openalex publication_date 2019/04/24 · arxiv updated 2019/04/30 · openalex updated_date 2026/08/05
), relying on evaluating parametric models relative to a nonparametric Bayesian reference using Kullback-Leibler divergence. D-probabilities are useful in goodness-of-fit assessments, in comparing imperfect models, and in providing model weights to be used in model aggregation. D-probabilities avoid some of the disadvantages of Bayesian model probabilities, such as large sensitivity to prior choice, and tend to place higher weight on a greater diversity of models. In an application to linear model selection against a Gaussian process reference, we provide simple analytic forms for routine implementation and show that D-probabilities automatically penalize model complexity. Some asymptotic properties are described, and we provide interesting probabilistic interpretations of the proposed model weights. The framework is illustrated through simulation examples and an ozone data application.