4 citations · 4 across the 3 of their papers we have counts for
3 papers
stat.ME2024
Ensemble Kalman inversion approximate Bayesian computation
Richard G Everitt
Approximate Bayesian computation (ABC) is the most popular approach to inferring parameters in the case where the data model is specified in the form of a simulator. It is not poss…
stat.ME2014★ 4 cited
Noisy Monte Carlo: Convergence of Markov chains with approximate transition kernels
P. Alquier, N. Friel, R. Everitt +1
Monte Carlo algorithms often aim to draw from a distribution by simulating a Markov chain with transition kernel such that is invariant under . However, there are ma…
stat.CO2012
Bayesian Parameter Estimation for Latent Markov Random Fields and Social Networks
Richard G. Everitt
Undirected graphical models are widely used in statistics, physics and machine vision. However Bayesian parameter estimation for undirected models is extremely challenging, since e…