Applied Bayesian Statistics summer school (ABS18 web site)
Villa del Grumello, Como, Italy
The topic chosen for the 2018 school is: Bayesian Statistical Modelling and Analysis in Sport
It is organized by
- IMATI CNR Istituto di Matematica Applicata e Tecnologie Informatiche, Consiglio Nazionale delle Ricerche
- Dipartimento di Scienze Statistiche Università Cattolica, Milano
The aim of this course is to increase students' ability to develop Bayesian models and computational solutions for real problems in the world of sport. A case study based teaching approach will be taken for the course. Each day, students will be presented with one or two problems posed by Sports Institutes regarding aspects of athlete training for world games. Through participatory problem solving, the students will be challenged to learn about theory, methods and applications of a range of Bayesian models including mixtures, spatio-temporal models, hidden Markov models and experimental design, and computational approaches including Markov chain Monte Carlo and Approximate Bayesian Computation. This hands-on course pays equivalent attention to theory and application, foundation and frontiers in Bayesian modelling and analysis. While the focus of the case studies is on sport, both sporting novices and lovers of sports are welcome, noting that the learning obtained in the course will be widely applicable to many other areas.
- Day 1: Lectures on introduction to Bayesian modelling and computation. Presentation of Problem 1: ranking and benchmarking athletes. Discussion and implementation of potential Bayesian hierarchical models and computational solutions. Communication of results.
- Day 2: Lectures on foundational Bayesian theory. Presentation of Problem 3: modelling swimmers' effective work per stroke. Discussion and implementation of potential Bayesian high dimensional regression models and computational solutions. Communication of results. Presentation of Problem 4: modelling cyclists' wearable data. Discussion and implementation of potential (marked) time series models and computational solutions. Communication of results.
- Day 3: Lectures on foundational Bayesian computation. Presentation of Problem 5: optimising athletes' resilience. Discussion and implementation of potential Bayesian mixture models to relate performance, fatigue and recovery. Communication of results.
- Day 4: Lectures on foundational Bayesian computation and frontier Bayesian theory. Presentation of Problem 6: optimal sampling strategies. Discussion and implementation of potential Bayesian experimental design methods for acquiring data from athletes. Presentation of Problem 7: using video data to compare planned and set play in team sports. Discussion and implementation of potential Bayesian spatio-temporal models. Communication of results.
- Day 5: Lectures on frontier Bayesian computation. Finalisation of problems 1-7. Extensions. Concluding remarks