This paper presents a simple idea for the use of quasi-Monte Carlo sampling with empirical datasets, such as those generated by MCMC methods. It also presents and analyses a related idea of taking advantage of the Hilbert space-filling curve. Theoretical and numerical analyses are provided for both. We find that when applying the proposed QMC sampling methods to datasets coming from a known distribution, they give similar performance as the standard QMC method directly sampling from this known distribution
The problem we consider is that of generating representative point sets from a distribution known up...
We introduce the Hamming ball sampler, a novel Markov chain Monte Carlo algorithm, for efficient inf...
Three sampling methods are compared for efficiency on a number of test problems of various complexit...
Abstract. Applied mathematics is concerned with developing models with predictive capability, and wi...
Applied mathematics is concerned with developing models with predictive capability, and with probing...
We use randomized quasi-Monte Carlo (RQMC) techniques to construct computational tools for working w...
International audienceWe survey basic ideas and results on randomized quasi-Monte Carlo (RQMC) metho...
MCMC sampling is a methodology that is becoming increasingly important in statistical signal process...
The performance of Markov chain Monte Carlo methods is often sensitive to the scaling and correlatio...
Many control problems are so complex that analytic techniques fail to solve them [2]. Furthermore, e...
This work aims at proposing a new method for estimating variances of complex survey estimators based...
Sampling methods use random values to simulate a distribution in order to compute integrals. We prop...
Markov Chain Monte Carlo (MCMC) algorithms play an important role in statistical inference problems ...
This article provides an overview of some interfaces between the theory of quasi-Monte Carlo (QMC) m...
This paper presents a Markov-Chain-Monte-Carlo (MCMC) procedure to sample uniformly from the collect...
The problem we consider is that of generating representative point sets from a distribution known up...
We introduce the Hamming ball sampler, a novel Markov chain Monte Carlo algorithm, for efficient inf...
Three sampling methods are compared for efficiency on a number of test problems of various complexit...
Abstract. Applied mathematics is concerned with developing models with predictive capability, and wi...
Applied mathematics is concerned with developing models with predictive capability, and with probing...
We use randomized quasi-Monte Carlo (RQMC) techniques to construct computational tools for working w...
International audienceWe survey basic ideas and results on randomized quasi-Monte Carlo (RQMC) metho...
MCMC sampling is a methodology that is becoming increasingly important in statistical signal process...
The performance of Markov chain Monte Carlo methods is often sensitive to the scaling and correlatio...
Many control problems are so complex that analytic techniques fail to solve them [2]. Furthermore, e...
This work aims at proposing a new method for estimating variances of complex survey estimators based...
Sampling methods use random values to simulate a distribution in order to compute integrals. We prop...
Markov Chain Monte Carlo (MCMC) algorithms play an important role in statistical inference problems ...
This article provides an overview of some interfaces between the theory of quasi-Monte Carlo (QMC) m...
This paper presents a Markov-Chain-Monte-Carlo (MCMC) procedure to sample uniformly from the collect...
The problem we consider is that of generating representative point sets from a distribution known up...
We introduce the Hamming ball sampler, a novel Markov chain Monte Carlo algorithm, for efficient inf...
Three sampling methods are compared for efficiency on a number of test problems of various complexit...