12 citations · 12 across the 2 of their papers we have counts for
5 papers
A fast non-reversible sampler for Bayesian finite mixture models
Filippo Ascolani, Giacomo Zanella
Finite mixtures 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…
Spectral gap of Metropolis-within-Gibbs under log-concavity
Cecilia Secchi, Giacomo Zanella
The Metropolis-within-Gibbs (MwG) algorithm is a widely used Markov Chain Monte Carlo method for sampling from high-dimensional distributions when exact conditional sampling is int…
On the fundamental limitations of multiproposal Markov chain Monte Carlo algorithms
Francesco Pozza, Giacomo Zanella
We study multiproposal Markov chain Monte Carlo algorithms, such as Multiple-try or generalised Metropolis-Hastings schemes, which have recently received renewed attention due to t…
Flexible Models for Microclustering with Application to Entity Resolution
Giacomo Zanella, Brenda Betancourt, Hanna Wallach +3
Most generative models for clustering implicitly assume that the number of data points in each cluster grows linearly with the total number of data points. Finite mixture models, D…
Bayesian complementary clustering, MCMC and Anglo-Saxon placenames
Giacomo Zanella
Common cluster models for multi-type point processes model the aggregation of points of the same type. In complete contrast, in the study of Anglo-Saxon settlements it is hypothesi…