mcmc

SOMA: a novel sampler for exchangeable variables

The problem of sampling exchangeable random variables arises in many Bayesian inference tasks, especially in data imputation given a privatized summary statistics. These permutation-invariant joint distributions often have dependency structures that …

Spectral gap bounds for reversible hybrid Gibbs chains

Hybrid Gibbs samplers represent a prominent class of approximated Gibbs algorithms that utilize Markov chains to approximate conditional distributions, with the Metropolis-within-Gibbs algorithm standing out as a well-known example. Despite their …

Data Augmentation MCMC: connections to privacy and advances in convergence analysis

SOMA: a novel sampler for exchangeable variables

SNP-Slice: A Bayesian nonparametric framework to resolve SNP haplotypes in mixed infections

Multi-strain infection is a common yet under-investigated phenomenon of many pathogens. Currently,biologists analyzing SNP information have to discard mixed infection samples,because existing downstream analyses require monogenomic infection …

Data Augmentation MCMC for Bayesian Inference from Privatized Data

Differentially private mechanisms protect privacy by introducing additional randomness into the data. Restricting access to only the privatized data makes it challenging to perform valid statistical inference on parameters underlying the confidential …