32 citations · 114 across the 17 of their papers we have counts for
21 papers
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…
Improving multiple-try Metropolis with local balancing
Philippe Gagnon, Florian Maire, Giacomo Zanella
Multiple-try Metropolis (MTM) is a popular Markov chain Monte Carlo method with the appealing feature of being amenable to parallel computing. At each iteration, it samples several…
Robust leave-one-out cross-validation for high-dimensional Bayesian models
Luca Silva, Giacomo Zanella
Leave-one-out cross-validation (LOO-CV) is a popular method for estimating out-of-sample predictive accuracy. However, computing LOO-CV criteria can be computationally expensive du…
Bayesian conjugacy in probit, tobit, multinomial probit and extensions: A review and new results
Niccolò Anceschi, Augusto Fasano, Daniele Durante +1
A broad class of models that routinely appear in several fields can be expressed as partially or fully discretized Gaussian linear regressions. Besides including basic Gaussian res…
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…
Optimal design of the Barker proposal and other locally-balanced Metropolis-Hastings algorithms
Jure Vogrinc, Samuel Livingstone, Giacomo Zanella
We study the class of first-order locally-balanced Metropolis--Hastings algorithms introduced in Livingstone & Zanella (2021). To choose a specific algorithm within the class the u…
Scalable Bayesian computation for crossed and nested hierarchical models
Omiros Papaspiliopoulos, Timothée Stumpf-Fétizon, Giacomo Zanella
We develop sampling algorithms to fit Bayesian hierarchical models, the computational complexity of which scales linearly with the number of observations and the number of paramete…
A fresh take on 'Barker dynamics' for MCMC
Max Hird, Samuel Livingstone, Giacomo Zanella
We study a recently introduced gradient-based Markov chain Monte Carlo method based on 'Barker dynamics'. We provide a full derivation of the method from first principles, placing…
Random Partition Models for Microclustering Tasks
Brenda Betancourt, Giacomo Zanella, Rebecca C. Steorts
Traditional Bayesian random partition models assume that the size of each cluster grows linearly with the number of data points. While this is appealing for some applications, this…
Scalable and Accurate Variational Bayes for High-Dimensional Binary Regression Models
Augusto Fasano, Daniele Durante, Giacomo Zanella
Modern methods for Bayesian regression beyond the Gaussian response setting are often computationally impractical or inaccurate in high dimensions. In fact, as discussed in recent…
The Barker proposal: combining robustness and efficiency in gradient-based MCMC
Samuel Livingstone, Giacomo Zanella
There is a tension between robustness and efficiency when designing Markov chain Monte Carlo (MCMC) sampling algorithms. Here we focus on robustness with respect to tuning paramete…
Scalable Importance Tempering and Bayesian Variable Selection
Giacomo Zanella, Gareth Roberts
We propose a Monte Carlo algorithm to sample from high dimensional probability distributions that combines Markov chain Monte Carlo and importance sampling. We provide a careful th…
Scalable inference for crossed random effects models
Omiros Papaspiliopoulos, Gareth O. Roberts, Giacomo Zanella
We analyze the complexity of Gibbs samplers for inference in crossed random effect models used in modern analysis of variance. We demonstrate that for certain designs the plain van…
Informed proposals for local MCMC in discrete spaces
Giacomo Zanella
There is a lack of methodological results to design efficient Markov chain Monte Carlo (MCMC) algorithms for statistical models with discrete-valued high-dimensional parameters. Mo…
Unbiased approximations of products of expectations
Anthony Lee, Simone Tiberi, Giacomo Zanella
We consider the problem of approximating the product of expectations with respect to a common probability distribution . Such products routinely arise in statistics as value…
A note on MCMC for nested multilevel regression models via belief propagation
Omiros Papaspiliopoulos, Giacomo Zanella
In the quest for scalable Bayesian computational algorithms we need to exploit the full potential of existing methodologies. In this note we point out that message passing algorith…
Multilevel linear models, Gibbs samplers and multigrid decompositions
Giacomo Zanella, Gareth Roberts
We study the convergence properties of the Gibbs Sampler in the context of posterior distributions arising from Bayesian analysis of conditionally Gaussian hierarchical models. We…
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…
A Dirichlet Form approach to MCMC Optimal Scaling
Giacomo Zanella, Wilfrid S. Kendall, Mylène Bédard
This paper develops the use of Dirichlet forms to deliver proofs of optimal scaling results for Markov chain Monte Carlo algorithms (specifically, Metropolis-Hastings random walk s…
Branching-stable point processes
Giacomo Zanella, Sergei Zuyev
The notion of stability can be generalised to point processes by defining the scaling operation in a randomised way: scaling a configuration by corresponds to letting such a co…
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…