International audienceWe propose a new algorithm for sampling the N-body density mid R:Psi(R)mid R:(2)R(3N)mid R:Psimid R:(2) in the variational Monte Carlo framework. This algorithm is based upon a modified Ricci-Ciccotti discretization of the Langevin dynamics in the phase space (R,P) improved by a Metropolis-Hastings accept/reject step. We show through some representative numerical examples (lithium, fluorine, and copper atoms and phenol molecule) that this algorithm is superior to the standard sampling algorithm based on the biased random walk (importance sampling)
The reduced density matrix of excitons coupled to a phonon bath at a finite temper-ature is studied ...
We introduce a variational algorithm to estimate the likelihood of a rare event within a nonequilibr...
International audiencePopulation Monte Carlo (PMC) algorithms are a family of adaptive importance sa...
International audienceWe propose a new algorithm for sampling the N-body density mid R:Psi(R)mid R:(...
The purpose of the present article is to compare different phase-space sampling methods, such as pu...
International audienceThe purpose of the present article is to compare different phase-space samplin...
We discuss several algorithms for sampling from unnormalized probability distributions in statistica...
The paper proposes Metropolis adjusted Langevin and Hamiltonian Monte Carlo sampling methods defined...
Building on the work of Iftimie et al., Boltzmann sampling of an approximate potential (the 'referen...
A multiscale, modular approach to protein sampling with novel Monte Carlo algorithms is is presented...
Monte Carlo (MC) algorithm aims to generate samples from a given probability distribution P (X) with...
We develop a sampling algorithm to explore the probability densities arising in Bayesian data analys...
Monte Carlo methods provide a power-ful framework for approximating proba-bility distributions with ...
I will present some numerical challenges raised by the simulation of materials at the atomistic leve...
Importance sampling methods can be iterated like MCMC algorithms, while being more robust against de...
The reduced density matrix of excitons coupled to a phonon bath at a finite temper-ature is studied ...
We introduce a variational algorithm to estimate the likelihood of a rare event within a nonequilibr...
International audiencePopulation Monte Carlo (PMC) algorithms are a family of adaptive importance sa...
International audienceWe propose a new algorithm for sampling the N-body density mid R:Psi(R)mid R:(...
The purpose of the present article is to compare different phase-space sampling methods, such as pu...
International audienceThe purpose of the present article is to compare different phase-space samplin...
We discuss several algorithms for sampling from unnormalized probability distributions in statistica...
The paper proposes Metropolis adjusted Langevin and Hamiltonian Monte Carlo sampling methods defined...
Building on the work of Iftimie et al., Boltzmann sampling of an approximate potential (the 'referen...
A multiscale, modular approach to protein sampling with novel Monte Carlo algorithms is is presented...
Monte Carlo (MC) algorithm aims to generate samples from a given probability distribution P (X) with...
We develop a sampling algorithm to explore the probability densities arising in Bayesian data analys...
Monte Carlo methods provide a power-ful framework for approximating proba-bility distributions with ...
I will present some numerical challenges raised by the simulation of materials at the atomistic leve...
Importance sampling methods can be iterated like MCMC algorithms, while being more robust against de...
The reduced density matrix of excitons coupled to a phonon bath at a finite temper-ature is studied ...
We introduce a variational algorithm to estimate the likelihood of a rare event within a nonequilibr...
International audiencePopulation Monte Carlo (PMC) algorithms are a family of adaptive importance sa...