One of the core problems of modern statistics is to approximate difficult-to-compute
probability densities. This problem is especially important in Bayesian statistics, which …

JuMP is an open-source modeling language that allows users to express a wide range of
optimization problems (linear, mixed-integer, quadratic, conic-quadratic, semidefinite, and …

The emerging orthogonal time frequency space (OTFS) modulation technique has shown its
superiority to the current orthogonal frequency division multiplexing (OFDM) scheme, in …

Black box variational inference allows researchers to easily prototype and evaluate an array
of models. Recent advances allow such algorithms to scale to high dimensions. However, a …

SUMMARY Markov chain Monte Carlo produces a correlated sample which may be used for
estimating expectations with respect to a target distribution. A fundamental question is: when …

Semi-implicit variational inference (SIVI) is introduced to expand the commonly used
analytic variational distribution family, by mixing the variational parameter with a flexible …

Mean-field Variational Bayes (MFVB) is an approximate Bayesian posterior inference
technique that is increasingly popular due to its fast runtimes on large-scale data sets …

Datasets are growing not just in size but in complexity, creating a demand for rich models
and quantification of uncertainty. Bayesian methods are an excellent fit for this demand, but …

Genetic association studies have yielded a wealth of biological discoveries. However, these
studies have mostly analyzed one trait and one SNP at a time, thus failing to capture the …

Abstract Motivation The results from Genome-Wide Association Studies (GWAS) on
thousands of phenotypes provide an unprecedented opportunity to infer the causal effect of …