  Probabilistic models often have parameters that can be translated, scaled,
permuted, or otherwise transformed without changing the model. These symmetries
can lead to strong correlation and multimodality in the posterior distribution
over the model's parameters, which can pose challenges both for performing
inference and interpreting the results. In this work, we address the automatic
detection of common problematic model symmetries. To do so, we introduce local
symmetries, which cover many common cases and are amenable to automatic
detection. We show how to derive algorithms to detect several broad classes of
local symmetries. Our algorithms are compatible with probabilistic programming
constructs such as arrays, for loops, and if statements, and they scale to
models with many variables.
