Slides

• An overview of my recent works on MCMC methodology. [pdf]
  
• Importance tempering of MCMC schemes (JSM) [pdf]
  
• Fast mixing MCMC for structure learning [pdf]
  

MCMC Methodology

• G. Li , A. Smith and Q. Zhou. Importance is important: a guide to informed importance tempering methods. Preprint. [arxiv]
• Q. Zhou and H. Chang. Complexity analysis of Bayesian learning of high-dimensional DAG models and their equivalence classes. Annals of Statistics, accepted. [link] [arxiv] [cs:graphical models]
• H. Chang* (NeurIPS Scholar Award), C. Lee*, Z. T. Luo, H. Sang and Q. Zhou. Rapidly mixing multiple-try Metropolis algorithms for model selection problems. NeurIPS (oral), 2022. [link] [arxiv]
• Q. Zhou, J. Yang, D. Vats, G. Roberts and J. Rosenthal. Dimension-free mixing for high-dimensional Bayesian variable selection. Journal of the Royal Statistical Society: Series B, 2022. [link] [arxiv] [cs:variable selection]
• Q. Zhou and A. Smith. Rapid convergence of informed importance tempering. AISTATS (oral, <3%), 2022. [link] [arxiv] [video]
• Q. Zhou and S. Zhou [ao]. Invited discussion on "Multilevel linear models, Gibbs samplers and multigrid decompositions" by G. Zanella and G. Roberts. Bayesian Analysis, 2021. [link]

Optimal Stopping and Stochastic Control

• T. De Angelis, J. Garg and Q. Zhou [ao]. A quickest detection problem with false negatives. Preprint. [arxiv]
• P. Ernst, M. Imerman, L. Shepp and Q. Zhou [ao]. Fiscal stimulus as an optimal control problem. Stochastic Processes and their Applications, 2021. [link] [arxiv]
• P. Ernst, G. Peskir and Q. Zhou [ao]. Optimal real-time detection of a drifting Brownian coordinate. Annals of Applied Probability, 2020. [link] [arxiv]
• S. Peng and Q. Zhou [ao]. A hypothesis-testing perspective on the G-normal distribution theory. Statistics and Probability Letters, 2020. [link] [arxiv]
• P. Ernst, L. C. G. Rogers and Q. Zhou [ao]. When is it best to follow the leader? Stochastic Processes and their Applications, 2020. [link] [arxiv]
• P. Ernst, L. C. G. Rogers and Q. Zhou [ao]. The value of foresight. Stochastic Processes and their Applications, 2017. [link] [arxiv]

Variable Selection

• G. Li and Q. Zhou. Bayesian multi-task variable selection with an application to differential DAG analysis. Journal of Graphical and Computational Statistics, accepted. [arxiv] [cs:graphical models]
• Q. Zhou, J. Yang, D. Vats, G. Roberts and J. Rosenthal. Dimension-free mixing for high-dimensional Bayesian variable selection. Journal of the Royal Statistical Society: Series B, 2022. [link] [arxiv] [cs:mcmc]
• Q. Zhou and Y. Guan. Fast model-fitting of Bayesian variable selection regression using the iterative complex factorization. Bayesian Analysis, 2018. [link] [arxiv] [software]

Graphical Models

• G. Li and Q. Zhou. Bayesian multi-task variable selection with an application to differential DAG analysis. Journal of Graphical and Computational Statistics, accepted. [arxiv] [cs:variable selection]
• H. Chang, J. Cai and Q. Zhou. Order-based structure learning without score equivalence. Biometrika, accepted. [arxiv]
• Q. Zhou and H. Chang. Complexity analysis of Bayesian learning of high-dimensional DAG models and their equivalence classes. Annals of Statistics, accepted. [link] [arxiv] [cs:mcmc]

Other Works in Statistics

• S. Jiang, Q. Zhou, X. Zhan and Q. Li. BayesSmiles: Bayesian segmentation modeling for longitudinal epidemiologoical studies. Journal of Data Science, 2021. [link] [medrxiv]
• Q. Zhou, P. Ernst, K. Morgan, D. Rubin and A. Zhang. Sequential rerandomization. Biometrika, 2018. [link] [arxiv]
• Q. Zhou. Asymptotics of multivariate contingency tables with fixed marginals. Journal of Statistical Planning and Inference, 2019. [link] [arxiv]
• Q. Zhou and Y. Guan. On the null distribution of Bayes factors in linear regression. Journal of the American Statistical Association, 2017. [pdf] [link] [software]

Other Works in Applied Probability

• P. Ernst, L. C. G. Rogers and Q. Zhou [ao]. The distribution of Yule's nonsense correlation. Preprint. [arxiv]
• P. Ernst, M. Kimmel, M. Kurpas and Q. Zhou [ao]. Heavy-tailed distributions in branching process models of secondary cancerous tumors. Advances in Applied Probability, 2018. [link] [arxiv]

Applied Research in Biology

• J. T. Atkinson*, A. M. Jones*, Q. Zhou and J. J. Silberg. Circular permutation on profiling by deep sequencing libraries created using transposon mutagenesis. Nucleic Acids Research, 2018. [link]
• D. He, Q. Zhu, Q. Zhou, et al. Correlation of serum MMP3 and other biomarkers with clinical outcomes in patients with ankylosing spondylitis: A pilot study. Clinical Rheumatology, 2017. [link]
• B. Zou, J. Li, Q. Zhou and Z. Quan. MIPE: a metagenome-based community structure explorer and SSU primer evaluation tool. PLoS ONE, 2017. [link]
• Q. Zhou, L. Zhao and Y. Guan. Strong selection at MHC in Mexicans since admixture. PLoS Genetics, 2016. [pdf] [link]
• D. Yu, M. Gadkari, Q. Zhou, et al. Postnatal epigenetic development of mouse intestinal stem cells requires DNA methylation and is guided by microbiome. Genome Biology, 2015. [link]
• D. Mao*, Q. Zhou*, C. Chen and Z. Quan. Coverage evaluation of universal bacterial primers using the metagenomic datasets. BMC Microbiology, 2012. [link]

Dissertation

• Theoretical and computational studies of Bayesian linear models. [pdf]