8 papers
Posterior concentration and adaptation of the mixing measure in Dirichlet process mixtures
Filippo Ascolani
We study the asymptotic properties of the posterior on the latent space for infinite mixtures driven by a Dirichlet process, both in terms of mixing measure and clustering behaviou…
Asymptotic regimes for maximum likelihood estimation in the Ewens--Pitman model: When the strength parameter matters
Filippo Ascolani, Mario Beraha, Stefano Favaro
We study the large sample asymptotic behaviour of the Maximum Likelihood Estimator of the discount and strength parameters in the Ewens--Pitman model for random partition…
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…
Entropy contraction of the Gibbs sampler under log-concavity
Filippo Ascolani, Hugo Lavenant, Giacomo Zanella
The Gibbs sampler (a.k.a. Glauber dynamics and heat-bath algorithm) is a popular Markov Chain Monte Carlo algorithm which iteratively samples from the conditional distributions of…
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…
A Conversation with Mike West
Hedibert F. Lopes, Filippo Ascolani
Mike West is currently the Arts & Sciences Distinguished Professor Emeritus of Statistics and Decision Sciences at Duke University. Mike's research in Bayesian analysis spans multi…