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Simulation-based Bayesian inference from privacy-protected data

Many modern statistical analysis and machine learning applications require training models on sensitive user data. Differential privacy provides a formal guarantee that individual-level information about users does not leak. In this framework, …

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 …

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 …

BETS: The dangers of selection bias in early analyses of the coronavirus disease (COVID-19) pandemic