ProxNest is an open source, well tested and documented Python implementation of the proximal nested sampling framework (Cai et al. 2022) to compute the Bayesian model evidence or marginal likelihood in high-dimensional log-convex settings. Furthermore, non-smooth sparsity-promoting priors are also supported.
This is achieved by exploiting tools from proximal calculus and Moreau-Yosida regularisation (Moreau 1962) to efficiently sample from the prior subject to the hard likelihood constraint. The resulting Markov chain iterations include a gradient step, approximating (with arbitrary precision) an overdamped Langevin SDE that can scale to very high-dimensional applications.
Matthew Price
Research Fellow in Artificial Intelligence and Imaging
My research considers information; where it is, how it can be extracted, and how we can use it.