Scholars@Duke
Filippo Ascolani

Filippo Ascolani

Assistant Professor of Statistical Science
Statistical Science
filippo.ascolani@duke.edu
214 Old Chemistry, Box 90251, Durham, NC 27708-0251
415 Chapel Drive, 214 Old Chemistry, Durham, NC 27708-0251

Selected Publications

An R Package for Nonparametric Inference on Dynamic Populations with Infinitely Many Types.

Journal Article Journal of computational biology : a journal of computational molecular cell biology · October 2024 Fleming-Viot diffusions are widely used stochastic models for population dynamics that extend the celebrated Wright-Fisher diffusions. They describe the temporal evolution of the relative frequencies of the allelic types in an ideally infinite panmictic po ... Full text Cite

Nonparametric priors with full-range borrowing of information

Journal Article Biometrika · September 1, 2024 Modelling of the dependence structure across heterogeneous data is crucial for Bayesian inference, since it directly impacts the borrowing of information. Despite extensive advances over the past two decades, most available methods only allow for nonnegati ... Full text Cite

DIMENSION-FREE MIXING TIMES OF GIBBS SAMPLERS FOR BAYESIAN HIERARCHICAL MODELS

Journal Article Annals of Statistics · June 1, 2024 Gibbs samplers are popular algorithms to approximate posterior distributions arising from Bayesian hierarchical models. Despite their popularity and good empirical performance, however, there are still relatively few quantitative results on their convergen ... Full text Cite

Clustering consistency with Dirichlet process mixtures

Journal Article Biometrika · May 15, 2023 SummaryDirichlet process mixtures are flexible nonparametric models, particularly suited to density estimation and probabilistic clustering. In this work we study the posterior distribution induced by Dirichlet process mixt ... Full text Cite