A Fully Bayesian Approach to Assessment of Model Adequacy in Inverse Problems
Sourabh Bhattacharya

TL;DR
This paper introduces a Bayesian goodness-of-fit test for inverse problems, using reference distributions derived from the posterior of unobserved covariates, with applications to palaeoclimatology.
Contribution
It proposes a novel Bayesian model adequacy assessment method based on inverse model components, with theoretical justification and practical demonstrations.
Findings
Effective in simulated examples
Successfully applied to high-dimensional palaeoclimate data
Provides a decision-theoretic framework for model fit assessment
Abstract
We consider the problem of assessing goodness of fit of a single Bayesian model to the observed data in the inverse problem context. A novel procedure of goodness of fit test is proposed, based on construction of reference distributions using the `inverse' part of the given model. This is motivated by an example from palaeoclimatology in which it is of interest to reconstruct past climates using information obtained from fossils deposited in lake sediment. Technically, given a model , where is the observed data and is a set of (non-random) covariates, we obtain reference distributions based on the posterior , where must be interpreted as the {\it unobserved} random vector corresponding to the {\it observed} covariates . Put simply, if the posterior distribution gives high density to the observed…
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