834 citations · 3.9k across the 95 of their papers we have counts for
48 papers · 1 filter
Sampling using Adaptive Regenerative Processes
Hector McKimm, Andi Q Wang, Murray Pollock +2
Enriching Brownian motion with regenerations from a fixed regeneration distribution at a particular regeneration rate results in a Markov process that has a target distribu…
Computing Bayes: From Then 'Til Now'
Gael M. Martin, David T. Frazier, Christian P. Robert
This paper takes the reader on a journey through the history of Bayesian computation, from the 18th century to the present day. Beginning with the one-dimensional integral first co…
The Importance Markov Chain
Charly Andral, Randal Douc, Hugo Marival +1
The Importance Markov chain is a novel algorithm bridging the gap between rejection sampling and importance sampling, moving from one to the other through a tuning parameter. Based…
Evidence estimation in finite and infinite mixture models and applications
Adrien Hairault, Christian P. Robert, Judith Rousseau
Estimating the model evidence - or mariginal likelihood of the data - is a notoriously difficult task for finite and infinite mixture models and we reexamine here different Monte C…
Rao-Blackwellization in the MCMC era
Christian P. Robert, Gareth O. Roberts
Rao-Blackwellization is a notion often occurring in the MCMC literature, with possibly different meanings and connections with the original Rao--Blackwell theorem (Rao, 1945 and Bl…
Computing Bayes: Bayesian Computation from 1763 to the 21st Century
Gael M. Martin, David T. Frazier, Christian P. Robert
The Bayesian statistical paradigm uses the language of probability to express uncertainty about the phenomena that generate observed data. Probability distributions thus characteri…
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…
Component-wise approximate Bayesian computation via Gibbs-like steps
Grégoire Clarté, Christian P. Robert, Robin Ryder +1
Approximate Bayesian computation methods are useful for generative models with intractable likelihoods. These methods are however sensitive to the dimension of the parameter space,…
Computational Solutions for Bayesian Inference in Mixture Models
Gilles Celeux, Kaniav Kamary, Gertraud Malsiner-Walli +2
This chapter surveys the most standard Monte Carlo methods available for simulating from a posterior distribution associated with a mixture and conducts some experiments about the…
Rethinking the Effective Sample Size
Víctor Elvira, Luca Martino, Christian P. Robert
The effective sample size (ESS) is widely used in sample-based simulation methods for assessing the quality of a Monte Carlo approximation of a given distribution and of related in…
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…
Approximating the Likelihood in Approximate Bayesian Computation
Christopher C Drovandi, Clara Grazian, Kerrie Mengersen +1
This chapter will appear in the forthcoming Handbook of Approximate Bayesian Computation (2018). The conceptual and methodological framework that underpins approximate Bayesian com…
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…
Auxiliary Likelihood-Based Approximate Bayesian Computation in State Space Models
Gael M. Martin, Brendan P. M. McCabe, David T. Frazier +2
A computationally simple approach to inference in state space models is proposed, using approximate Bayesian computation (ABC). ABC avoids evaluation of an intractable likelihood b…
On Consistency of Approximate Bayesian Computation
David T. Frazier, Gael M. Martin, Christian P. Robert
Approximate Bayesian computation (ABC) methods have become increasingly prevalent of late, facilitating as they do the analysis of intractable, or challenging, statistical problems…
The Metropolis-Hastings algorithm
Christian P. Robert
This short note is a self-contained and basic introduction to the Metropolis-Hastings algorithm, this ubiquitous tool used for producing dependent simulations from an arbitrary dis…
Accelerating Metropolis-Hastings algorithms by Delayed Acceptance
Marco Banterle, Clara Grazian, Anthony Lee +1
MCMC algorithms such as Metropolis-Hastings algorithms are slowed down by the computation of complex target distributions as exemplified by huge datasets. We offer in this paper a…
Bayesian computation: a perspective on the current state, and sampling backwards and forwards
Peter J. Green, Krzysztof Łatuszyński, Marcelo Pereyra +1
The past decades have seen enormous improvements in computational inference based on statistical models, with continual enhancement in a wide range of computational tools, in compe…
Accelerating Metropolis-Hastings algorithms: Delayed acceptance with prefetching
Marco Banterle, Clara Grazian, Christian P. Robert
MCMC algorithms such as Metropolis-Hastings algorithms are slowed down by the computation of complex target distributions as exemplified by huge datasets. We offer in this paper an…
Pre-processing for approximate Bayesian computation in image analysis
Matthew T. Moores, Christopher C. Drovandi, Kerrie Mengersen +1
Most of the existing algorithms for approximate Bayesian computation (ABC) assume that it is feasible to simulate pseudo-data from the model at each iteration. However, the computa…
Importance sampling schemes for evidence approximation in mixture models
Jeong Eun Lee, Christian P. Robert
The marginal likelihood is a central tool for drawing Bayesian inference about the number of components in mixture models. It is often approximated since the exact form is unavaila…
Efficient learning in ABC algorithms
Mohammed Sedki, Pierre Pudlo, Jean-Michel Marin +2
Approximate Bayesian Computation has been successfully used in population genetics to bypass the calculation of the likelihood. These methods provide accurate estimates of the post…