13 citations · 29 across the 6 of their papers we have counts for
6 papers · 1 filter
Markov Chain Monte Carlo Methods, a survey with some frequent misunderstandings
Christian P. Robert, Wu Changye
In this chapter, we review some of the most standard MCMC tools used in Bayesian computation, along with vignettes on standard misunderstandings of these approaches taken from Q \&…
Parallelising MCMC via Random Forests
Wu Changye, Christian P. Robert
For Bayesian computation in big data contexts, the divide-and-conquer MCMC concept splits the whole data set into batches, runs MCMC algorithms separately over each batch to produc…
Faster Hamiltonian Monte Carlo by Learning Leapfrog Scale: an offline randomized solution
Changye Wu, Pierre Pudlo, Christian P. Robert +1
We introduce a Hamiltonian Monte Carlo (HMC) methodology based on an offline empirical calibration of randomized leapfrog parameters. The approach, referred to as eHMC, where \text…
Accelerating MCMC Algorithms
Christian P. Robert, Victor Elvira, Nick Tawn +1
Markov chain Monte Carlo algorithms are used to simulate from complex statistical distributions by way of a local exploration of these distributions. This local feature avoids heav…
Generalized Bouncy Particle Sampler
Changye Wu, Christian P. Robert
As a special example of piecewise deterministic Markov process, bouncy particle sampler is a rejection-free, irreversible Markov chain Monte Carlo algorithm and can draw samples fr…
Average of Recentered Parallel MCMC for Big Data
Changye Wu, Christian P. Robert
In big data context, traditional MCMC methods, such as Metropolis-Hastings algorithms and hybrid Monte Carlo, scale poorly because of their need to evaluate the likelihood over the…