Hi, I'm Antonio, a Ph.D. candidate in Statistics. If my teaching materials or code have been useful, you can support the work with a coffee.
I develop Bayesian time-series methods for probabilistic forecasting, with an emphasis on quantile modeling, uncertainty quantification, scalable inference, and reusable research software.
Much of this work is motivated by hydrological and environmental forecasting, where forecasts need to be calibrated, interpretable, computationally practical, and easy to update as new data arrive.
Bayesian quantile methods for correcting river-flow forecasts, combining forecast sources, and evaluating predictive distributions with proper scoring rules.
Bayesian quantile readouts for fixed Deep Echo State Network features, with regularized readouts, exAL working likelihoods, MCMC, and variational approximations.
Flexible dynamic quantile linear models with trend, seasonal, regression, transfer-function, forecasting, diagnostics, and posterior synthesis components.
Variational Bayes, Sequential Monte Carlo, simulation diagnostics, and reproducible workflows for models that need to run repeatedly and be checked carefully.
Best Poster Prize
My poster Bayesian quantile-based correction and synthesis of climate products received a Best Poster Prize at the ISBA 2026 World Meeting in Nagoya, Japan. The work presents a Bayesian quantile workflow for correcting climate-product forecasts and synthesizing the corrected quantile lanes into a posterior predictive distribution, with daily San Lorenzo River flow as the case study.
Selected package code, manuscript-support scripts, and data-processing workflows are collected on the Software page. I keep that page selective so each public example has a clear purpose and enough context to be useful.