6 papers
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…
Central Limit Theorem for ergodic averages of Markov chains \& the comparison of sampling algorithms for heavy-tailed distributions
Miha Brešar, Aleksandar Mijatović, Gareth Roberts
Establishing central limit theorems (CLTs) for ergodic averages of Markov chains is a fundamental problem in probability and its applications. Since the seminal work~\cite{MR834478…
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…