Embedded Files
Summer School 2026
Summer School 2026
Lectures
Lectures
An Introduction to Sequential Decision Problems
(Mandatory for the first-year Ph.D. Students)
Speaker: Prof. Justin Goodson, Saint Louis University (USA)
Wednesday, May 20th, 2026, 10:00 AM
Room A6, Contrada S. Chiara, 50
Abstract
This short lecture introduces stochastic dynamic programming as a framework for modeling and solving sequential decision problems under uncertainty. Participants will learn how to formulate a problem as a Markov decision process, interpret decision trees, and understand the value function and backward induction. Because exact solutions are often impractical, the session surveys practical policy design strategies, including policy, cost, and value function approximations, as well as direct lookaheads. The lecture also introduces dual bounds and information relaxations as tools for benchmarking policies and quantifying optimality gaps. Examples from dynamic delivery and routing illustrate how these ideas translate into implementable decision rules in practice.
Bayesian Nonparametrics: Ideas, Models and Computation
(Mandatory for the first-year Ph.D. Students)
Speaker: Prof. Bernardo Nipoti, Università degli Studi Milano-Bicocca (Italy)
Tuesday, May 26th, 2026, 10:30 AM - 12:30 PM
Tuesday, May 26th, 2026, 14:00 PM - 16:00 PM
Sala Biblioteca, Via S. Faustino 74/b
Abstract
Bayesian nonparametric inference has become an increasingly important paradigm in modern statistics, providing a flexible and principled framework for probabilistic modeling and data analysis. By placing prior distributions on infinite-dimensional objects, Bayesian nonparametric methods allow for accurate estimation of complex functions such as probability distributions, regression functions, and hazard rates. Rooted in de Finetti’s foundational work on exchangeability, the field has evolved through key methodological developments, most notably the introduction of the Dirichlet process, and has undergone substantial growth over the last two decades, driven by advances in computational algorithms and their successful application to a wide range of challenging problems.
This short course provides an introduction to Bayesian Nonparametric Statistics at the PhD level. It begins with a concise review of the Bayesian paradigm, with emphasis on probabilistic modeling, prior specification, posterior inference, and computation. The assumption of exchangeability is then introduced as a key modeling principle in Bayesian inference, providing a rationale for the use of prior distributions. The Dirichlet process is presented as the most prominent Bayesian nonparametric prior, along with its main theoretical properties and constructive representations. Building on this framework, the course focuses on Dirichlet process mixture models as flexible tools for density estimation and clustering. Extensions beyond the Dirichlet process are subsequently discussed, with particular attention to the Pitman–Yor process and its implications for modeling power-law behavior and richer clustering structures. Computational aspects of Bayesian nonparametrics are addressed throughout the course, including Markov chain Monte Carlo methods as well as marginal and conditional sampling schemes. The theoretical concepts are complemented by concrete examples and hands-on implementations in R, with specific use of the BNPmix package for fitting and exploring Bayesian nonparametric mixture
models.
Workshop
Workshop
Optimization Models for Energy Economics
(Mandatory for the first-year Ph.D. Students)
Speaker: Prof. Antonio J. Conejo,The Ohio State University, USA.
Speaker: Prof. Miguel Carrión Ruiz Peinado, Universidad de Castilla - La Mancha, Spain.
Speaker: Prof. Álavaro García Cerezo, Universidad de Castilla - La Mancha, Spain.
Chair: Prof. Ruth Dominguez, University of Brescia
Thursday, May 21st, 2026, 09:30 AM
Room A5, Contrada Santa Chiara, 50
Seminars 2026
Seminars 2026
Bounded Backward Induction for Max-Min Dynamic Programs
(Mandatory for the first-year Ph.D. Students)
Speaker: Prof. Justin Goodson, Saint Louis University (USA)
Chair: Prof. Luca Bertazzi, University of Brescia
Wednesday, May 27th, 2026, 11:00 AM
Sala Biblioteca, Via San Faustino 74/b
Solving the Consistent Travelling Salesman Problem
(Mandatory for the first-year Ph.D. Students)
Speaker: Prof. Juan José Salazar González, Universidad de la Laguna, Tenerife
Chair: Prof. Carlo Filippi, University of Brescia
Wednesday, May 20th, 2026, 02:30 PM
Room D2, Via San Faustino 64 (Brixia Building)
Designing tournaments in sports: balancing chance and fairness
(Mandatory for the first-year Ph.D. Students)
Speaker: Prof. Frits Spieksma, Eindhoven University of Technology, The Netherlands.
Chair: Prof. Carlo Filippi, University of Brescia
Wednesday, May 6th, 2026, 11:30 AM
Room D4, Via San Faustino 64 (Brixia Building)
Price of Diversity: the case of the TSP
(Mandatory for the first-year Ph.D. Students)
Speaker: Prof. Frits Spieksma, Eindhoven University of Technology, The Netherlands.
Chair: Prof. Carlo Filippi, University of Brescia
Wednesday, April 29th, 2026, 2:30 PM
Room D2, Via San Faustino 64 (Brixia Building)
Energy communities: technologies, regulations, and economics. The Spanish case.
(Mandatory for the first-year Ph.D. Students)
Speaker: Prof. Luis Varela Cabo, Universidade de Santiago de Compostela (USC), Spain.
Chair: Prof. Paolo Falbo, University of Brescia
Thursday, February 12th, 2026, 12:30 PM
Room D5, Via San Faustino 64 (Brixia Building)
Great Layoff, Great Retirement and Post-Pandemic Inflation
Speaker: Prof. Domenico Massaro, University of Milan, IT
Chair: Prof. Mattia Guerini, University of Brescia
Wednesday, January 21st, 2026, 01:30 P.M.
Room D2, Via San Faustino 64
Abstract
This paper examines how pandemic-induced layoffs contributed to post-Covid-19 inflation through their effects on retirement and labor supply. Using CPS microdata, we show that the unprecedented “Great Layoff” triggered a sharp rise in early retirements – the “Great Retirement” – which increased labor market tightness and nominal wages. Younger non-participants were drawn into employment, partly offsetting the loss of older workers. To quantify this mechanism, we estimate a New Keynesian model with endogenous participation and retirement. Counterfactual simulations show that the Great Retirement accounted for roughly three cumulative percentage points of inflation from 2020 to 2024, with modest GDP effects.
Introduction to intervention meta-analysis
Speaker: Prof. Marta Pellegrini, University of Cagliari, IT
Chair: Prof. Giovanni Maria Abbiati, University of Brescia
Thursday, January 22nd, 2026, 3-6 P.M.
Friday, January 23rd, 2026, 9-12 P.M.
Room D2, Via San Faustino 64 (both seminars)
Page updated
Report abuse