303 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Politecnico di TorinoIT17 papers
- Sapienza University of RomeIT12 papers
- University of TurinIT10 papers
- Centre National de la Recherche ScientifiqueFR9 papers
- Collegio Carlo AlbertoIT9 papers
- Decision Sciences (United States)US7 papers
- École Polytechnique Fédérale de LausanneCH7 papers
- The Abdus Salam International Centre for Theoretical Physics (ICTP)IT7 papers
- The University of Texas at AustinUS7 papers
- University of MilanIT7 papers
- Institute for Complex SystemsIT6 papers
- Istituto Nazionale di Fisica NucleareIT6 papers
12 papers · 1 filter
On randomized step sizes in Metropolis-Hastings algorithms
Sebastiano Grazzi, Samuel Livingstone, Lionel Riou-Durand
The performance of Metropolis-Hastings algorithms is highly sensitive to the choice of step size, and miss-specification can lead to severe loss of efficiency. We study algorithms…
Linear-cost unbiased posterior estimates for crossed effects and matrix factorization models via couplings
Paolo Maria Ceriani, Andrea Pandolfi, Giacomo Zanella
We design and analyze unbiased Markov chain Monte Carlo (MCMC) schemes based on couplings of blocked Gibbs samplers (BGSs), whose total computational costs scale linearly with the…
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…
BNPdensity: Bayesian nonparametric mixture modeling in R
Julyan Arbel, Guillaume Kon Kam King, Antonio Lijoi +2
Robust statistical data modelling under potential model mis-specification often requires leaving the parametric world for the nonparametric. In the latter, parameters are infinite…
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…
Variational Bayes for Gaussian Factor Models under the Cumulative Shrinkage Process
Sirio Legramanti
The cumulative shrinkage process is an increasing shrinkage prior that can be employed within models in which additional terms are supposed to play a progressively negligible role.…
Computing Shapley Effects for Sensitivity Analysis
Elmar Plischke, Giovanni Rabitti, Emanuele Borgonovo
Shapley effects are attracting increasing attention as sensitivity measures. When the value function is the conditional variance, they account for the individual and higher order e…
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…
A nested expectation-maximization algorithm for latent class models with covariates
Daniele Durante, Antonio Canale, Tommaso Rigon
We develop a nested EM routine for latent class models with covariates which allows maximization of the full-model log-likelihood and, differently from current methods, guarantees…
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…
A moment-matching Ferguson and Klass algorithm
Julyan Arbel, Igor Prünster
Completely random measures (CRM) represent the key building block of a wide variety of popular stochastic models and play a pivotal role in modern Bayesian Nonparametrics. A popula…
Towards automatic calibration of the number of state particles within the SMC algorithm
Nicolas Chopin, James Ridgway, Mathieu Gerber +1
SMC is an efficient algorithm for sequential estimation and state inference of state-space models. It generates parameter particles , and, for each , it run…