57 citations · 156 across the 32 of their papers we have counts for
7 papers · 1 filter
Perfecting MCMC Sampling: Recipes and Reservations
Radu V. Craiu, Xiao-Li Meng
This review paper is intended for the Handbook of Markov chain Monte Carlo's second edition. The authors will be grateful for any suggestions that could perfect it.
Approximate Methods for Bayesian Computation
Radu V. Craiu, Evgeny Levi
Rich data generating mechanisms are ubiquitous in this age of information and require complex statistical models to draw meaningful inference. While Bayesian analysis has seen enor…
Double Happiness: Enhancing the Coupled Gains of L-lag Coupling via Control Variates
Radu V. Craiu, Xiao-Li Meng
The recently proposed L-lag coupling for unbiased Markov chain Monte Carlo (MCMC) calls for a joint celebration by MCMC practitioners and theoreticians. For practitioners, it circu…
Finding our Way in the Dark: Approximate MCMC for Approximate Bayesian Methods
Evgeny Levi, Radu V. Craiu
With larger data at their disposal, scientists are emboldened to tackle complex questions that require sophisticated statistical models. It is not unusual for the latter to have li…
Adaptive Component-wise Multiple-Try Metropolis Sampling
Jinyoung Yang, Evgeny Levi, Radu V. Craiu +1
One of the most widely used samplers in practice is the component-wise Metropolis-Hastings (CMH) sampler that updates in turn the components of a vector valued Markov chain using a…
Embarrassingly Parallel Sequential Markov-chain Monte Carlo for Large Sets of Time Series
Roberto Casarin, Radu V. Craiu, Fabrizio Leisen
Bayesian computation crucially relies on Markov chain Monte Carlo (MCMC) algorithms. In the case of massive data sets, running the Metropolis-Hastings sampler to draw from the post…
A Mixture-Based Approach to Regional Adaptation for MCMC
Radu V. Craiu, Antonio Fabio Di Narzo
Recent advances in adaptive Markov chain Monte Carlo (AMCMC) include the need for regional adaptation in situations when the optimal transition kernel is different across different…