3 papers
math.PR2025
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…
stat.CO2024
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…
stat.ML2024
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…