142 citations · 600 across the 47 of their papers we have counts for
5 papers · 1 filter
Deep Compositional Spatial Models
Andrew Zammit-Mangion, Tin Lok James Ng, Quan Vu +1
Spatial processes with nonstationary and anisotropic covariance structure are often used when modelling, analysing and predicting complex environmental phenomena. Such processes ma…
Assessing Bayesian Nonparametric Log-Linear Models: an application to Disclosure Risk estimation
Cinzia Carota, Maurizio Filippone, Silvia Polettini
We present a method for identification of models with good predictive performances in the family of Bayesian log-linear mixed models with Dirichlet process random effects. Such a p…
Pseudo-extended Markov chain Monte Carlo
Christopher Nemeth, Fredrik Lindsten, Maurizio Filippone +1
Sampling from posterior distributions using Markov chain Monte Carlo (MCMC) methods can require an exhaustive number of iterations, particularly when the posterior is multi-modal a…
Enabling scalable stochastic gradient-based inference for Gaussian processes by employing the Unbiased LInear System SolvEr (ULISSE)
Maurizio Filippone, Raphael Engler
In applications of Gaussian processes where quantification of uncertainty is of primary interest, it is necessary to accurately characterize the posterior distribution over covaria…
Bayesian Inference for Gaussian Process Classifiers with Annealing and Pseudo-Marginal MCMC
Maurizio Filippone
Kernel methods have revolutionized the fields of pattern recognition and machine learning. Their success, however, critically depends on the choice of kernel parameters. Using Gaus…