Related papers: Comment on ``Monte Carlo Algorithms with Absorbing…
We introduce a new micro-macro Markov chain Monte Carlo method (mM-MCMC) to sample invariant distributions of molecular dynamics systems that exhibit a time-scale separation between the microscopic (fast) dynamics, and the macroscopic…
The objective of this review article is to present recent results on the mathematical analysis of the Accelerated Dynamics algorithms introduced by A.F. Voter in collaboration with D. Perez and M. Sorensen. Using the notion of…
A Comment on the paper "Conservative Quantum Computing" by M. Ozawa, Phys. Rev. Lett. 89, 057902 (2002). The author replies in Phys. Rev. Lett. 91, 089802 (2003).
A Comment on the Letter by C. R. Galley, Phys. Rev. Lett. 110, 174301 (2013).
We compare the results of our two papers with the results of the paper Aratyn H., Gomes J.F., Zimerman A.H., Higher order Painlev\'e equations and their symmetries via reductions of a class of integrable models, J. Phys. A: Math. Theor., V.…
We propose an efficient Markov Chain Monte Carlo method for sampling equilibrium distributions for stochastic lattice models, capable of handling correctly long and short-range particle interactions. The proposed method is a Metropolis-type…
Delayed-acceptance Markov chain Monte Carlo (DA-MCMC) samples from a probability distribution via a two-stages version of the Metropolis-Hastings algorithm, by combining the target distribution with a "surrogate" (i.e. an approximate and…
Monte Carlo methods are widely used to estimate observables in many-body quantum systems. However, conventional sampling schemes often require a large number of samples to achieve sufficient accuracy. In this work we propose the…
It has become increasingly easy nowadays to collect approximate posterior samples via fast algorithms such as variational Bayes, but concerns exist about the estimation accuracy. It is tempting to build solutions that exploit approximate…
We comment on the recent paper "Large-Scale Monte Carlo Study of a Realistic Lattice Model for Ga_(1-x)Mn_xAs" by Y. Yildirim, G. Alvarez, A. Moreo and E. Dagotto", Phys Rev. Lett.{\bf 99}, 057207 (2007); cond-mat/0612002
In this brief report we pointed at mistake in paper A. Zholents, Damping Force in the Transit-Time Method of Optical Stochastic Cooling, PRLST. Mar 1, 2012. 2 pp. Published in Phys.Rev.ST Accel. Beams 15 (2012) 032801.
In these lectures, given in '96 summer schools in Beg-Rohu (France) and Budapest, I discuss the fundamental principles of thermodynamic and dynamic Monte Carlo methods in a simple light-weight fashion. The keywords are MARKOV CHAINS,…
The book "Continuous-Time Markov Chains" by W. J. Anderson collects a large part of the development in the past thirty years. It is now a popular reference for the researchers on this subject or related fields. Unfortunately, due to a…
Adaptive Markov chain Monte Carlo (MCMC) algorithms, which automatically tune their parameters based on past samples, have proved extremely useful in practice. The self-tuning mechanism makes them `non-Markovian', which means that their…
We merge in this note our two discussions about the Read Paper "Particle Markov chain Monte Carlo" (Andrieu, Doucet, and Holenstein, 2010) presented on October 16th 2009 at the Royal Statistical Society, appearing in the Journal of the…
We prove explicit, i.e., non-asymptotic, error bounds for Markov Chain Monte Carlo methods, such as the Metropolis algorithm. The problem is to compute the expectation (or integral) of f with respect to a measure which can be given by a…
This is a reply to the comment by J. L. Alonso et al. (arXiv:1302.6146; to appear in J. Chem. Phys) on our paper A. Abedi, N. T. Maitra, and E. K. U. Gross, J. Chem. Phys. 137, 22A530 (2012) [arXiv: 1208.4388]
After carefully checking the original Russian version of Kolmogorov (1942), in the present author view, both Spalding and Wilcox misinterpretations are due to the fact that they did not notice the two different symbols, which Kolmogorov…
In this paper we propose an efficient variance reduction approach for additive functionals of Markov chains relying on a novel discrete time martingale representation. Our approach is fully non-asymptotic and does not require the knowledge…
Stochastic gradient Markov Chain Monte Carlo algorithms are popular samplers for approximate inference, but they are generally biased. We show that many recent versions of these methods (e.g. Chen et al. (2014)) cannot be corrected using…