6 citations · 11 across the 3 of their papers we have counts for
7 papers · 1 filter
Measure Transport with Kernel Stein Discrepancy
Matthew A. Fisher, Tui Nolan, Matthew M. Graham +2
Measure transport underpins several recent algorithms for posterior approximation in the Bayesian context, wherein a transport map is sought to minimise the Kullback--Leibler diver…
Ensemble MCMC: Accelerating Pseudo-Marginal MCMC for State Space Models using the Ensemble Kalman Filter
Christopher Drovandi, Richard G Everitt, Andrew Golightly +1
Particle Markov chain Monte Carlo (pMCMC) is now a popular method for performing Bayesian statistical inference on challenging state space models (SSMs) with unknown static paramet…
Black-box Variational Inference for Stochastic Differential Equations
Thomas Ryder, Andrew Golightly, A. Stephen McGough +1
Parameter inference for stochastic differential equations is challenging due to the presence of a latent diffusion process. Working with an Euler-Maruyama discretisation for the di…
Marginal sequential Monte Carlo for doubly intractable models
Richard G. Everitt, Dennis Prangle, Philip Maybank +1
Bayesian inference for models that have an intractable partition function is known as a doubly intractable problem, where standard Monte Carlo methods are not applicable. The past…
gk: An R Package for the g-and-k and generalised g-and-h Distributions
Dennis Prangle
The g-and-k and (generalised) g-and-h distributions are flexible univariate distributions which can model highly skewed or heavy tailed data through only four parameters: location…
An ABC interpretation of the multiple auxiliary variable method
Dennis Prangle, Richard G. Everitt
We show that the auxiliary variable method (Møller et al., 2006; Murray et al., 2006) for inference of Markov random fields can be viewed as an approximate Bayesian computation met…