Bijectors.jl: Flexible transformations for probability distributions
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Proceedings of The 2nd Symposium on
Advances in Approximate Bayesian Inference, PMLR 118:117, 2020.
Abstract
Transforming one probability distribution to another is a powerful tool in Bayesian inference and machine learning. Some prominent examples are constrainedtounconstrained transformations of distributions for use in Hamiltonian Monte Carlo and constructing exible and learnable densities such as normalizing ows. We present Bijectors.jl, a software package in Julia for transforming distributions, available at github.com/TuringLang/Bijectors.jl. The package provides a exible and composable way of implementing transformations of distributions without being tied to a computational framework. We demonstrate the use of Bijectors.jl on improving variational inference by encoding known statistical dependencies into the variational posterior using normalizing ows, providing a general approach to relaxing the meanfield assumption usually made in variational inference.
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