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…
A deterministic multiple-shift lattice algorithm for function approximation in Korobov and half-period Cosine spaces
Jiarui Du, Josef Dick
Approximating multivariate periodic functions in weighted Korobov spaces via rank-1 lattices is fundamentally limited by frequency aliasing. Existing optimal-rate methods rely on r…
Randomized Quasi-Monte Carlo and Importance Sampling for Super-Fast Growing Functions with Applications to Finance
Jianlong Chen, Yu Xu, Jiarui Du +1
Many problems can be formulated as high-dimensional integrals of discontinuous functions that exhibit significant boundary growth, challenging the error analysis and applications o…
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…