3 papers
math.ST2026
Strong log-concavity in probit regression
Martin Chak, Giacomo Zanella, Zoraida F. Rico
We show that strong log-concavity emerges in probit regression likelihoods without ridge penalization (i.e. Gaussian priors), unlike for the logistic case. Specifically, we provide…
stat.CO2026
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…
stat.CO2025
Complexity of Markov Chain Monte Carlo for Generalized Linear Models
Martin Chak, Giacomo Zanella
Markov Chain Monte Carlo (MCMC), Laplace approximation (LA) and variational inference (VI) methods are popular approaches to Bayesian inference, each with trade-offs between comput…