31 citations · 51 across the 12 of their papers we have counts for
5 papers · 1 filter
A fast non-reversible sampler for Bayesian mixture models
Filippo Ascolani, Giacomo Zanella, Paolo Manildo
Mixtures models are a cornerstone of Bayesian modelling, and it is well-known that sampling from the resulting posterior distribution can be a hard task. In particular, popular rev…
Mixing times of data-augmentation Gibbs samplers for high-dimensional probit regression
Filippo Ascolani, Giacomo Zanella
We investigate the convergence properties of popular data-augmentation samplers for Baye\-sian probit regression. Leveraging recent results on Gibbs samplers for log-concave target…
An R package for nonparametric inference on dynamic populations with infinitely many types
Filippo Ascolani, Stefano Damato, Matteo Ruggiero
Fleming-Viot diffusions are widely used stochastic models for population dynamics which extend the celebrated Wright-Fisher diffusions. They describe the temporal evolution of the…
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…
Dimension-free mixing times of Gibbs samplers for Bayesian hierarchical models
Filippo Ascolani, Giacomo Zanella
Gibbs samplers are popular algorithms to approximate posterior distributions arising from Bayesian hierarchical models. Despite their popularity and good empirical performances, ho…