Add to ORKGWe describe a Langevin diffusion with a target stationary density with respect to Lebesgue measure, as opposed to the volume measure of a previously-proposed diffusion. The two are sometimes equivalent but in general distinct and lead to different Metropolis-adjusted Langevin algorithms, which we compare. © 2014 Elsevier B.V
We address the problem of simulating efficiently from the posterior distribution over the parameters...
This thesis focuses on the problem of sampling in high dimension and is based on the unadjusted Lang...
8 pages, 4 figures; v2: Reference addedWe study the consequences of different realizations of diffus...
We describe a Langevin diffusion with a target stationary density with respect to Lebesgue measure, ...
We consider a class of Langevin diffusions with state-dependent volatility. The volatility of the di...
The Metropolis-adjusted Langevin (MALA) algorithm is a sampling algorithm which makes local moves by...
International audienceThis paper considers the optimal scaling problem for high-dimensional random w...
Markov chain Monte Carlo (MCMC) methods are a cornerstone of Bayesian inference and stochastic simul...
This paper proposes an adaptive version for the Metropolis adjusted Langevin algorithm with a trunca...
In our work we propose new Exact Algorithms for simulation of diffusions with discontinuous drift an...
Recent optimal scaling theory has produced a condition for the asymptotically optimal acceptance rat...
It has been shown that the nonreversible overdamped Langevin dynamics enjoy better convergence prope...
In this paper, we study the computational complexity of sampling from a Bayesian posterior (or pseud...
This paper proposes a new sampling scheme based on Langevin dynamics that is applicable within pseud...
The problem of biological motion is a very intriguing and topical issue. Many efforts are being foc...
We address the problem of simulating efficiently from the posterior distribution over the parameters...
This thesis focuses on the problem of sampling in high dimension and is based on the unadjusted Lang...
8 pages, 4 figures; v2: Reference addedWe study the consequences of different realizations of diffus...
We describe a Langevin diffusion with a target stationary density with respect to Lebesgue measure, ...
We consider a class of Langevin diffusions with state-dependent volatility. The volatility of the di...
The Metropolis-adjusted Langevin (MALA) algorithm is a sampling algorithm which makes local moves by...
International audienceThis paper considers the optimal scaling problem for high-dimensional random w...
Markov chain Monte Carlo (MCMC) methods are a cornerstone of Bayesian inference and stochastic simul...
This paper proposes an adaptive version for the Metropolis adjusted Langevin algorithm with a trunca...
In our work we propose new Exact Algorithms for simulation of diffusions with discontinuous drift an...
Recent optimal scaling theory has produced a condition for the asymptotically optimal acceptance rat...
It has been shown that the nonreversible overdamped Langevin dynamics enjoy better convergence prope...
In this paper, we study the computational complexity of sampling from a Bayesian posterior (or pseud...
This paper proposes a new sampling scheme based on Langevin dynamics that is applicable within pseud...
The problem of biological motion is a very intriguing and topical issue. Many efforts are being foc...
We address the problem of simulating efficiently from the posterior distribution over the parameters...
This thesis focuses on the problem of sampling in high dimension and is based on the unadjusted Lang...
8 pages, 4 figures; v2: Reference addedWe study the consequences of different realizations of diffus...