  A common strategy for inference in complex models is the relaxation of a
simple model into the more complex target model, for example the prior into the
posterior in Bayesian inference. Existing approaches that attempt to generate
such transformations, however, are sensitive to the pathologies of complex
distributions and can be difficult to implement in practice. Leveraging the
geometry of thermodynamic processes I introduce a principled and robust
approach to deforming measures that presents a powerful new tool for inference.
