Hierarchical Bayesian data selection

TL;DR

This paper introduces hierarchical Bayesian data selection to improve model inference by simultaneously estimating model parameters and data inclusion, effectively identifying data regions well-represented by the model.

Contribution

It presents a novel hierarchical Bayesian approach for data selection that enhances inference accuracy and robustness across various modeling scenarios.

Findings

Effective in linear regression problems

Improves mixing of Markov chain sampling

Applicable to ODE model fitting

Abstract

There are many issues that can cause problems when attempting to infer model parameters from data. Data and models are both imperfect, and as such there are multiple scenarios in which standard methods of inference will lead to misleading conclusions; corrupted data, models which are only representative of subsets of the data, or multiple regions in which the model is best fit using different parameters. Methods exist for the exclusion of some anomalous types of data, but in practice, data cleaning is often undertaken by hand before attempting to fit models to data. In this work, we will employ hierarchical Bayesian data selection; the simultaneous inference of both model parameters, and parameters which represent our belief that each observation within the data should be included in the inference. The aim, within a Bayesian setting, is to find the regions of observation space for which the model can well-represent the data, and to find the corresponding model parameters for those regions. A number of approaches will be explored, and applied to test problems in linear regression, and to the problem of fitting an ODE model, approximated by a finite difference method. The approaches are simple to implement, can aid mixing of Markov chains designed to sample from the arising densities, and are broadly applicable to many inferential problems.