Geoscientists use observed data to estimate properties of the Earth's interior. This often
requires non‐linear inverse problems to be solved and uncertainties to be estimated …

Variational inference has become an increasingly attractive fast alternative to Markov chain
Monte Carlo methods for approximate Bayesian inference. However, a major obstacle to the …

We develop EigenVI, an eigenvalue-based approach for black-box variational inference
(BBVI). EigenVI constructs its variational approximations from orthogonal function …

Many scientific investigations require that the values of a set of model parameters are
estimated using recorded data. In Bayesian inference, information from both observed data …

Most leading implementations of black-box variational inference (BBVI) are based on
optimizing a stochastic evidence lower bound (ELBO). But such approaches to BBVI often …

Bayesian coresets have emerged as a promising approach for implementing scalable
Bayesian inference. The Bayesian coreset problem involves selecting a (weighted) subset of …

Boosting variational inference (BVI) approximates an intractable probability density by
iteratively building up a mixture of simple component distributions one at a time, using …

This work presents mixed variational flows (MixFlows), a new variational family that consists
of a mixture of repeated applications of a map to an initial reference distribution. First, we …

Bayesian decision theory outlines a rigorous framework for making optimal decisions based
on maximizing expected utility over a model posterior. However, practitioners often do not …

A common assumption in causal modeling posits that the data is generated by a set of
independent mechanisms, and algorithms should aim to recover this structure. Standard …