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 …

Variational Inference (VI) is a popular alternative to asymptotically exact sampling in
Bayesian inference. Its main workhorse is optimization over a reverse Kullback-Leibler …

Generative probabilistic models of biological sequences have widespread existing and
potential applications in analyzing, predicting and designing proteins, RNA and genomes …

Black-box variational inference tries to approximate a complex target distribution through a
gradient-based optimization of the parameters of a simpler distribution. Provable …

Variational autoencoder (VAE) is a deep generative model for unsupervised learning,
allowing to encode observations into the meaningful latent space. VAE is prone to …

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

Approximating a probability density in a tractable manner is a central task in Bayesian
statistics. Variational Inference (VI) is a popular technique that achieves tractability by …

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 …