4 citations · 14 across the 19 of their papers we have counts for
19 papers
A spectral gap for Metropolis-adjusted Langevin algorithm with a uniformly randomized step size
Qian Qin
Let on , where is continuously differentiable and -strongly convex with a globally -Lipschitz gradient, $0…
Spectral Gap for the Binary Fixed-Margin Swap Chain
Weibo Fu, Qian Qin, Guanyang Wang
We prove an explicit spectral-gap lower bound for the lazy swap chain on binary matrices with prescribed row and column sums. This chain is a standard sampler for fixed-margin null…
Solidarity of Spectral Gaps for Component-Wise Markov Chains
Youngwoo Kwon, Galin Jones, Qian Qin
Deterministic-scan and random-scan component-wise Markov chain Monte Carlo algorithms, such as Gibbs samplers and conditional Metropolis-Hastings, are popular approaches for sampli…
Convergence analysis of data augmentation algorithms in Bayesian lasso models with log-concave likelihoods
Jingkai Cui, Qian Qin
We study the convergence properties of a class of data augmentation algorithms targeting posterior distributions of Bayesian lasso models with log-concave likelihoods. Leveraging i…
Convergence Bounds for Monte Carlo Markov Chains
Qian Qin
This review paper, written for the second edition of the Handbook of Markov Chain Monte Carlo, provides an introduction to the study of convergence analysis for Markov chain Monte…
Multivariate strong invariance principle and uncertainty assessment for time in-homogeneous cyclic MCMC samplers
Haoxiang Li, Qian Qin
Time in-homogeneous cyclic Markov chain Monte Carlo (MCMC) samplers, including deterministic scan Gibbs samplers and Metropolis within Gibbs samplers, are extensively used for samp…
Geometric ergodicity of trans-dimensional Markov chain Monte Carlo algorithms
Qian Qin
This article studies the convergence properties of trans-dimensional MCMC algorithms when the total number of models is finite. It is shown that, for reversible and some non-revers…
Convergence Analysis of Data Augmentation Algorithms for Bayesian Robust Multivariate Linear Regression with Incomplete Data
Haoxiang Li, Qian Qin, Galin L. Jones
Gaussian mixtures are commonly used for modeling heavy-tailed error distributions in robust linear regression. Combining the likelihood of a multivariate robust linear regression m…
Spectral Telescope: Convergence Rate Bounds for Random-Scan Gibbs Samplers Based on a Hierarchical Structure
Qian Qin, Guanyang Wang
Random-scan Gibbs samplers possess a natural hierarchical structure. The structure connects Gibbs samplers targeting higher dimensional distributions to those targeting lower dimen…
Analysis of two-component Gibbs samplers using the theory of two projections
Qian Qin
The theory of two projections is utilized to study two-component Gibbs samplers. Through this theory, previously intractable problems regarding the asymptotic variances of two-comp…
Convergence Rates of Two-Component MCMC Samplers
Qian Qin, Galin L. Jones
Component-wise MCMC algorithms, including Gibbs and conditional Metropolis-Hastings samplers, are commonly used for sampling from multivariate probability distributions. A long-sta…
On the limitations of single-step drift and minorization in Markov chain convergence analysis
Qian Qin, James P. Hobert
Over the last three decades, there has been a considerable effort within the applied probability community to develop techniques for bounding the convergence rates of general state…
Geometric convergence bounds for Markov chains in Wasserstein distance based on generalized drift and contraction conditions
Qian Qin, James P. Hobert
Let denote a Markov chain on a Polish space that has a stationary distribution . This article concerns upper bounds on the Wasserstein distance between…
Wasserstein-based methods for convergence complexity analysis of MCMC with applications
Qian Qin, James P. Hobert
Over the last 25 years, techniques based on drift and minorization (d&m) have been mainstays in the convergence analysis of MCMC algorithms. However, results presented herein sugge…
Convergence complexity analysis of Albert and Chib's algorithm for Bayesian probit regression
Qian Qin, James P. Hobert
The use of MCMC algorithms in high dimensional Bayesian problems has become routine. This has spurred so-called convergence complexity analysis, the goal of which is to ascertain h…
Estimating the spectral gap of a trace-class Markov operator
Qian Qin, James P. Hobert, Kshitij Khare
The utility of a Markov chain Monte Carlo algorithm is, in large part, determined by the size of the spectral gap of the corresponding Markov operator. However, calculating (and ev…
Trace-class Monte Carlo Markov Chains for Bayesian Multivariate Linear Regression with Non-Gaussian Errors
Qian Qin, James P. Hobert
Let denote the intractable posterior density that results when the likelihood from a multivariate linear regression model with errors from a scale mixture of normals is combine…
On the Data Augmentation Algorithm for Bayesian Multivariate Linear Regression with Non-Gaussian Errors
Qian Qin, James P. Hobert
Let denote the intractable posterior density that results when the likelihood from a multivariate linear regression model with errors from a scale mixture of normals is combine…
Convergence Analysis of the Data Augmentation Algorithm for Bayesian Linear Regression with Non-Gaussian Errors
James P. Hobert, Yeun Ji Jung, Kshitij Khare +1
Gaussian errors are sometimes inappropriate in a multivariate linear regression setting because, for example, the data contain outliers. In such situations, it is often assumed tha…