5 papers
The -error rate for randomized quasi-Monte Carlo self-normalized importance sampling of unbounded integrands
Jiarui Du, Zhijian He
Self-normalized importance sampling (SNIS) is a fundamental tool in Bayesian inference when the posterior distribution involves an unknown normalizing constant. In many application…
Uncertainty quantification using importance-sampled quasi-Monte Carlo with dimension-independent convergence rates
Zexin Pan, Du Ouyang, Zhijian He
Quasi-Monte Carlo (QMC) integration over unbounded domains remains challenging due to the high dimensionality of sampling space and the boundary growth of the integr…
Density estimation via periodic scaled Korobov kernel method with exponential decay condition
Ziyang Ye, Haoyuan Tan, Xiaoqun Wang +1
We propose the periodic scaled Korobov kernel (PSKK) method for nonparametric density estimation on . By first wrapping the target density into a periodic version thr…
Enhanced convergence rates of Adaptive Importance Sampling with recycling schemes via quasi-Monte Carlo methods
Jianlong Chen, Jiarui Du, Xiaoqun Wang +1
This article investigates the integration of quasi-Monte Carlo (QMC) methods using the Adaptive Multiple Importance Sampling (AMIS). Traditional Importance Sampling (IS) often suff…
Unbiased Markov chain quasi-Monte Carlo for Gibbs samplers
Jiarui Du, Zhijian He
In statistical analysis, Monte Carlo (MC) stands as a classical numerical integration method. When encountering challenging sample problem, Markov chain Monte Carlo (MCMC) is a com…