activity
20152020
most citedStability of Noisy Metropolis-Hastings

2 citations · 2 across the 1 of their papers we have counts for

collaborators

5 papers

stat.CO2020

Penalised t-walk MCMC

Felipe J Medina-Aguayo, J Andrés Christen

Handling multimodality that commonly arises from complicated statistical models remains a challenge. Current Markov chain Monte Carlo (MCMC) methodology tackling this subject is ba…

stat.CO2019

Revisiting the balance heuristic for estimating normalising constants

Felipe J Medina-Aguayo, Richard G Everitt

Multiple importance sampling estimators are widely used for computing intractable constants due to its reliability and robustness. The celebrated balance heuristic estimator belong…

stat.CO2018

Perturbation Bounds for Monte Carlo within Metropolis via Restricted Approximations

Felipe Medina-Aguayo, Daniel Rudolf, Nikolaus Schweizer

The Monte Carlo within Metropolis (MCwM) algorithm, interpreted as a perturbed Metropolis-Hastings (MH) algorithm, provides an approach for approximate sampling when the target dis…

stat.CO2016

Sequential Monte Carlo with transformations

Richard G Everitt, Richard Culliford, Felipe Medina-Aguayo +1

This paper introduces methodology for performing Bayesian inference sequentially on a sequence of posteriors on spaces of different dimensions. We show how this may be achieved thr…

stat.CO2015★ 2 cited

Stability of Noisy Metropolis-Hastings

Felipe J. Medina-Aguayo, Anthony Lee, Gareth O. Roberts

Pseudo-marginal Markov chain Monte Carlo methods for sampling from intractable distributions have gained recent interest and have been theoretically studied in considerable depth.…