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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.CO2024
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…