6 citations · 6 across the 4 of their papers we have counts for
4 papers
Foundations of locally-balanced Markov processes
Samuel Livingstone, Giorgos Vasdekis, Giacomo Zanella
We formally introduce and study locally-balanced Markov jump processes (LBMJPs) defined on a general state space. These continuous-time stochastic processes with a user-specified l…
Conjugate gradient methods for high-dimensional GLMMs
Andrea Pandolfi, Omiros Papaspiliopoulos, Giacomo Zanella
Generalized linear mixed models (GLMMs) are a widely used tool in statistical analysis. The main bottleneck of many computational approaches lies in the inversion of the high dimen…
Optimal and computationally tractable lower bounds for logistic log-likelihoods
Niccolò Anceschi, Cristian Castiglione, Tommaso Rigon +2
The logit transform is arguably the most widely-employed link function beyond linear settings. This transformation routinely appears in regression models for binary data and provid…
Partially factorized variational inference for high-dimensional mixed models
Max Goplerud, Omiros Papaspiliopoulos, Giacomo Zanella
While generalized linear mixed models are a fundamental tool in applied statistics, many specifications, such as those involving categorical factors with many levels or interaction…