I work on convergence analysis of Markov chains and the theory of Markov chain Monte Carlo (MCMC). If you are interested in convergence analysis for MCMC, you can take a look at this book chapter I wrote.
Articles
W. Fu, Q. Qin, G. Wang (2026). Spectral gap for the binary fixed-margin swap chain. arXiv.
Y. Kwon, G. L. Jones, Q. Qin (2026). Solidarity of spectral gaps for component-wise Markov chains. arXiv.
Y. Kwon, Q. Qin, G. Wang, Y. Wei (2026). A phase transition in sampling from Restricted Boltzmann Machines, Annals of Applied Probability. arXiv.
J. Cui, Q. Qin (2025). Convergence analysis of data augmentation algorithms in Bayesian lasso models with log-concave likelihoods. arXiv.
Q. Qin (2025). On spectral gap decomposition for Markov chains. arXiv.
Q. Qin (2025). Geometric ergodicity of trans-dimensional Markov chain Monte Carlo algorithms, Journal of the American Statistical Association. arXiv. Code.
Q. Qin, N. Ju, G. Wang (2025). Spectral gap bounds for reversible hybrid Gibbs chains, Annals of Statistics. arXiv.
S. Wang, S. Chakraborty, Q. Qin, R. Bai (2024). Neural-g: A deep learning framework for mixing density estimation. arXiv.
H. Li, Q. Qin (2024). Multivariate strong invariance principle and uncertainty assessment for time in-homogeneous cyclic MCMC samplers. arXiv.
Q. Qin (2024). Analysis of two-component Gibbs samplers using the theory of two projections, Annals of Applied Probability. arXiv.
H. Li, Q. Qin, G. L. Jones (2024). Convergence analysis of data augmentation algorithms for Bayesian robust multivariate linear regression with incomplete data, Journal of Multivariate Analysis. arXiv.
Q. Qin, G. Wang (2024). Spectral telescope: Convergence rate bounds for random-scan Gibbs samplers based on a hierarchical structure, Annals of Applied Probability. arXiv.
Q. Qin, J. P. Hobert (2022). Geometric convergence bounds for Markov chains in Wasserstein distance based on generalized drift and contraction conditions, Annales de l’Institut Henri Poincaré (B) Probabilités et Statistiques. arXiv.
G. L. Jones, Q. Qin (2022). Markov chain Monte Carlo in Practice, Annual Review of Statistics and Its Application. Link.
Q. Qin, G. L. Jones (2022). Convergence rates of two-component MCMC samplers, Bernoulli. arXiv.
Q. Qin, J. P. Hobert (2022). Wasserstein-based methods for convergence complexity analysis of MCMC with applications, Annals of Applied Probability. arXiv.
Q. Qin, J. P. Hobert (2021). On the limitations of single-step drift and minorization in Markov chain convergence analysis, Annals of Applied Probability. arXiv.
Q. Qin, J. P. Hobert (2019). Estimating the spectral gap of a trace-class Markov operator, Electronic Journal of Statistics. arXiv. Code.
Q. Qin, J. P. Hobert (2019). Convergence complexity analysis of Albert and Chib's algorithm for Bayesian probit regression, Annals of Statistics. arXiv.
J. P. Hobert, Y. J. Jung, K. Khare, Q. Qin (2018). Convergence analysis of MCMC algorithms for Bayesian multivariate linear regression with non-Gaussian errors, Scandinavian Journal of Statistics. Link.
Q. Qin, J. P. Hobert (2018). Trace-class Monte Carlo Markov chains for Bayesian multivariate linear regression with non-Gaussian errors, Journal of Multivariate Analysis. arXiv.