  Combining predictions from different models is a central problem in Bayesian
inference and machine learning more broadly. Currently, these predictive
distributions are almost exclusively combined using linear mixtures such as
Bayesian model averaging, Bayesian stacking, and mixture of experts. Such
linear mixtures impose idiosyncrasies that might be undesirable for some
applications, such as multi-modality. While there exist alternative strategies
(e.g. geometric bridge or superposition), optimising their parameters usually
involves computing an intractable normalising constant repeatedly. We present
two novel Bayesian model combination tools. These are generalisations of model
stacking, but combine posterior densities by log-linear pooling (locking) and
quantum superposition (quacking). To optimise model weights while avoiding the
burden of normalising constants, we investigate the Hyvarinen score of the
combined posterior predictions. We demonstrate locking with an illustrative
example and discuss its practical application with importance sampling.
