23 citations · 36 across the 6 of their papers we have counts for
6 papers
Markov chain Monte Carlo importance samplers for Bayesian models with intractable likelihoods
Jordan Franks
We consider the efficient use of an approximation within Markov chain Monte Carlo (MCMC), with subsequent importance sampling (IS) correction of the Markov chain inexact output, le…
On the use of approximate Bayesian computation Markov chain Monte Carlo with inflated tolerance and post-correction
Matti Vihola, Jordan Franks
Approximate Bayesian computation allows for inference of complicated probabilistic models with intractable likelihoods using model simulations. The Markov chain Monte Carlo impleme…
Unbiased inference for discretely observed hidden Markov model diffusions
Neil K. Chada, Jordan Franks, Ajay Jasra +2
We develop a Bayesian inference method for diffusions observed discretely and with noise, which is free of discretisation bias. Unlike existing unbiased inference methods, our meth…
Importance sampling correction versus standard averages of reversible MCMCs in terms of the asymptotic variance
Jordan Franks, Matti Vihola
We establish an ordering criterion for the asymptotic variances of two consistent Markov chain Monte Carlo (MCMC) estimators: an importance sampling (IS) estimator, based on an app…
Importance sampling type estimators based on approximate marginal MCMC
Matti Vihola, Jouni Helske, Jordan Franks
We consider importance sampling (IS) type weighted estimators based on Markov chain Monte Carlo (MCMC) targeting an approximate marginal of the target distribution. In the context…
On 1-cocycles induced by a positive definite function on a locally compact abelian group
Jordan Franks, Alain Valette
For a normalized positive definite function on a locally compact abelian group , we consider on the one hand the unitary representation associated to by the GNS co…