1 citations · 1 across the 7 of their papers we have counts for
9 papers
Weak Poincaré inequalities for two-component Deterministic-scan Metropolis-within-Gibbs samplers
Mengxi Gao, Gareth O. Roberts, Andi Q. Wang
Using the framework of weak Poincaré inequalities, we analyze the convergence properties of deterministic-scan Metropolis-within-Gibbs samplers, an important class of Markov chain…
On micromodes in Bayesian posterior distributions and their implications for MCMC
Sanket Agrawal, Sebastiano Grazzi, Gareth O. Roberts
We investigate the existence and severity of local modes in posterior distributions from Bayesian analyses. These are known to occur in posterior tails resulting from heavy-tailed…
Ergodic Central limit theorem for subgeometric Markov chains
Miha Brešar, Aleksandar Mijatović, Gareth Roberts
We establish verifiable necessary conditions for central limit theorems (CLTs) of ergodic averages of Markov chains on general state spaces. Our criteria are based on discrete-time…
Transient regime of piecewise deterministic Monte Carlo algorithms
Sanket Agrawal, Joris Bierkens, Kengo Kamatani +1
Piecewise Deterministic Markov Processes (PDMPs) such as the Bouncy Particle Sampler and the Zig-Zag Sampler, have gained attention as continuous-time counterparts of classical Mar…
Non-centering for discrete-valued state transition models: an application to ESBL-producing E. coli transmission in Malawi
James Neill, Rebecca Lester, Winnie Bakali +4
Infectious disease transmission is often modelled by discrete-valued stochastic state-transition processes. Due to a lack of complete data, Bayesian inference for these models ofte…
Exact Bayesian inference for Markov switching diffusions
Timothée Stumpf-Fétizon, Krzysztof Łatuszyński, Jan Palczewski +1
We develop the first exact Bayesian methodology for the problem of inference in discretely observed regime switching diffusions. Switching diffusion models extend ordinary diffusio…
Large sample scaling analysis of the Zig-Zag algorithm for Bayesian inference
Sanket Agrawal, Joris Bierkens, Gareth O. Roberts
Piecewise deterministic Markov processes provide scalable methods for sampling from the posterior distributions in big data settings by admitting principled sub-sampling strategies…
Scalability of Metropolis-within-Gibbs schemes for high-dimensional Bayesian models
Filippo Ascolani, Gareth O. Roberts, Giacomo Zanella
We study general coordinate-wise MCMC schemes (such as Metropolis-within-Gibbs samplers), which are commonly used to fit Bayesian non-conjugate hierarchical models. We relate their…
Unbiased Monte Carlo: posterior estimation for intractable/infinite-dimensional models
Sergios Agapiou, Gareth O. Roberts, Sebastian J. Vollmer
We provide a general methodology for unbiased estimation for intractable stochastic models. We consider situations where the target distribution can be written as an appropriate li…