5 papers
Pathwise skew-symmetric discretisation for SDEs with superlinear drift
Yuga Iguchi, Samuel Livingstone, Giorgos Vasdekis +1
The skew-symmetric discretisation has recently been proposed as a new robust simulation method for weakly approximating stochastic differential equations (SDEs) with non-globally L…
Some aspects of robustness in modern Markov Chain Monte Carlo
Sam Power, Giorgos Vasdekis
Markov Chain Monte Carlo (MCMC) is a flexible approach to approximate sampling from intractable probability distributions, with a rich theoretical foundation and comprising a wealt…
Skew-symmetric schemes for stochastic differential equations with non-Lipschitz drift: an unadjusted Barker algorithm
Yuga Iguchi, Samuel Livingstone, Nikolas Nüsken +2
We propose a new simple and explicit numerical scheme for time-homogeneous stochastic differential equations. The scheme is based on sampling increments at each time step from a sk…
Foundations of locally-balanced Markov processes
Samuel Livingstone, Giorgos Vasdekis, Giacomo Zanella
We formally introduce and study locally-balanced Markov jump processes (LBMJPs) defined on a general state space. These continuous-time stochastic processes with a user-specified l…
Sampling with time-changed Markov processes
Andrea Bertazzi, Giorgos Vasdekis
We study time-changed Markov processes to speed up the convergence of Markov chain Monte Carlo (MCMC) algorithms. The time-changed process is defined by adjusting the speed of time…