Neal and Hinton (1998) recast maximum likelihood estimation of any given latent variable
model as the minimization of a free energy functional F, and the EM algorithm as coordinate …

Stochastic gradient descent is an optimisation method that combines classical gradient
descent with random subsampling within the target functional. In this work, we introduce the …

One main limitation of the existing optimal scaling results for Metropolis–Hastings algorithms
is that the assumptions on the target distribution are unrealistic. In this paper, we consider …

The problem of optimally scaling the proposal distribution in a Markov chain Monte Carlo
algorithm is critical to the quality of the generated samples. Much work has gone into …

There has been a recent surge of interest in coupling methods for Markov chain Monte Carlo
algorithms: they facilitate convergence quantification and unbiased estimation, while …

For sufficiently smooth targets of product form it is known that the variance of a single
coordinate of the proposal in RWM (random walk Metropolis) and MALA (Metropolis …

Aiming at the issue of UAV coordinated scheduling, this paper attempts to construct a UAV
scheduling model based on an online coordination platform from the perspective of task …

For a noisy spin system, we derive a nonlocal stochastic version of the overdamped Landau-
Lipshitz equation designed to respect the underlying Hamiltonian structure and sample the …

Abstract The Metropolis-adjusted Langevin algorithm (MALA) is an informed MCMC method
that is used to sample from a target distribution of interest. Its proposal distribution makes …

We study the macroscopic behavior of a stochastic spin ensemble driven by a discrete
Markov jump process motivated by the Metropolis-Hastings algorithm where the proposal is …