13 citations · 20 across the 4 of their papers we have counts for
3 papers · 1 filter
Distributed Computation for Marginal Likelihood based Model Choice
Alexander Buchholz, Daniel Ahfock, Sylvia Richardson
We propose a general method for distributed Bayesian model choice, using the marginal likelihood, where a data set is split in non-overlapping subsets. These subsets are only acces…
Adaptive Tuning Of Hamiltonian Monte Carlo Within Sequential Monte Carlo
Alexander Buchholz, Nicolas Chopin, Pierre E. Jacob
Sequential Monte Carlo (SMC) samplers form an attractive alternative to MCMC for Bayesian computation. However, their performance depends strongly on the Markov kernels used to rej…
Improving approximate Bayesian computation via quasi-Monte Carlo
Alexander Buchholz, Nicolas Chopin
ABC (approximate Bayesian computation) is a general approach for dealing with models with an intractable likelihood. In this work, we derive ABC algorithms based on QMC (quasi- Mon…