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ABS18: Bayesian Statistical Modelling and Analysis in Sport by Kerrie Mengersen

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ABS18: Bayesian Statistical Modelling and Analysis in Sport by Kerrie Mengersen

Δημοσίευση από grstats Την / Το Τετ 8 Νοε 2017 - 10:37

Applied Bayesian Statistics summer school 2018

I am glad to inform you about the next school, ABS18, to be held at Villa
del Grumello, Como, Italy, on June 4-8, 2018. We are working on the website
but you can find information on location and fees (unchanged in 2018 with
respect to the last few years) at the ABS17 website

http://www.mi.imati.cnr.it/conferences/abs17/home.htm

If you are interested in being updated about ABS18, please send me a
message. Otherwise, you will get a new message from me next year about
ABS19.

Best regards, Fabrizio Ruggeri

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TITLE: Bayesian Statistical Modelling and Analysis in Sport

LECTURER: Kerrie Mengersen
Queensland University of Technology, Brisbane, Australia
http://staff.qut.edu.au/staff/mengerse/

ABOUT THE LECTURER: Distinguished Professor Kerrie Mengersen is the current
ISBA (International Society for Bayesian Analysis) President. She is author
of over 200 refereed journal publications, supervisor of over 30
postgraduate students in the past 5 years, recipient of over 30 large
research grants. In
2016 she was awarded the Pitman Medal, the highest honour to be presented by
the Statistical Society of Australia and the first woman to receive it. More
details on her outstanding career can be found at her webpage.

COURSE OUTLINE: 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.

DAILY ACTIVITIES

* 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.



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