31 citations · 51 across the 12 of their papers we have counts for
14 papers
Asymptotics and finite sample bounds for prediction and smoothing in Wright-Fisher hidden Markov models
Luigi M. Malgieri, Filippo Ascolani, Matteo Giordano +1
We study signal prediction and smoothing in hidden Markov models whose latent signal is a multi-type Wright-Fisher diffusion and whose discrete-time observations are categorical. W…
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 partitions,…
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…
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…
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…
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…
Nonparametric priors with full-range borrowing of information
Filippo Ascolani, Beatrice Franzolini, Antonio Lijoi +1
Modeling of the dependence structure across heterogeneous data is crucial for Bayesian inference since it directly impacts the borrowing of information. Despite the extensive advan…
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…
Clustering consistency with Dirichlet process mixtures
Filippo Ascolani, Antonio Lijoi, Giovanni Rebaudo +1
Dirichlet process mixtures are flexible non-parametric models, particularly suited to density estimation and probabilistic clustering. In this work we study the posterior distribut…
Smoothing distributions for conditional Fleming-Viot and Dawson-Watanabe diffusions
Filippo Ascolani, Antonio Lijoi, Matteo Ruggiero
We study the distribution of the unobserved states of two measure-valued diffusions of Fleming-Viot and Dawson-Watanabe type, conditional on observations from the underlying popula…
Predictive inference with Fleming--Viot-driven dependent Dirichlet processes
Filippo Ascolani, Antonio Lijoi, Matteo Ruggiero
We consider predictive inference using a class of temporally dependent Dirichlet processes driven by Fleming--Viot diffusions, which have a natural bearing in Bayesian nonparametri…