Antonio Aguirre Data Modelling·Bayesian Statistics·Machine Learning

Research Overview

I am currently a Ph.D. candidate in the Department of Statistics at the University of California, Santa Cruz, under the guidance of Bruno Sanso and Raquel Prado. My research is focused on advancing Bayesian methods to address challenges in spatio-temporal modeling and real-time forecasting. I am particularly interested in developing interpretable and operational models for hydrology and environmental sciences, as well as integrating modern computational approaches for scalable Bayesian inference.

Ongoing Projects

  • Real-Time Forecast Synthesis: Developing methods to integrate and correct diverse climate models for robust hydrological predictions and uncertainty quantification.
  • Quantile State-Space Modelling: Advancing Fast and Flexible Bayesian quantile-based methods for river discharge modeling and other environmental applications.
  • Scalable Bayesian Inference: Combining Sequential Monte Carlo techniques with Variational Bayes for efficient modeling of dynamic systems.

Recent Publications

  • Aguirre, A., Lobato, I.N. (2024). Evidence of Non-Fundamentalness in OECD Capital Stocks. Empirical Economics. [DOI]