  When building statistical models for Bayesian data analysis tasks, required
and optional iterative adjustments and different modelling choices can give
rise to numerous candidate models. In particular, checks and evaluations
throughout the modelling process can motivate changes to an existing model or
the consideration of alternative models to ultimately obtain models of
sufficient quality for the problem at hand. Additionally, failing to consider
alternative models can lead to overconfidence in the predictive or inferential
ability of a chosen model. The search for suitable models requires modellers to
work with multiple models without jeopardising the validity of their results.
Multiverse analysis offers a framework for transparent creation of multiple
models at once based on different sensible modelling choices, but the number of
candidate models arising in the combination of iterations and possible
modelling choices can become overwhelming in practice. Motivated by these
challenges, this work proposes iterative filtering for multiverse analysis to
support efficient and consistent assessment of multiple models and meaningful
filtering towards fewer models of higher quality across different modelling
contexts. Given that causal constraints have been taken into account, we show
how multiverse analysis can be combined with recommendations from established
Bayesian modelling workflows to identify promising candidate models by
assessing predictive abilities and, if needed, tending to computational issues.
We illustrate our suggested approach in different realistic modelling scenarios
using real data examples.
