Bayesian Meta-Reasoning: Determining Model Adequacy from Within a Small World
This addresses the challenge of model validation in statistical inference, particularly for researchers and practitioners needing to evaluate models without explicit alternatives, though it appears incremental as it builds on existing Bayesian and asymptotic methods.
The paper tackles the problem of assessing model adequacy without needing to specify an alternative model, by developing a Bayesian framework and a test statistic to track model performance across repeated instances, with asymptotic methods used to derive its approximate distribution.
This paper presents a Bayesian framework for assessing the adequacy of a model without the necessity of explicitly enumerating a specific alternate model. A test statistic is developed for tracking the performance of the model across repeated problem instances. Asymptotic methods are used to derive an approximate distribution for the test statistic. When the model is rejected, the individual components of the test statistic can be used to guide search for an alternate model.