August 16, 2022 - August 17, 2022
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Model Calibration in R (Virtual)
LEVEL: Intermediate
TRACK: Methodological & Statistical Research
LENGTH: 4 Hours | Course runs 2 consecutive days, 2 hours each day
Register Here
10:00AM-12:00PM Eastern Daylight Time (EDT)
09:00AM-11:00AM Central Daylight Time (CDT)
14:00PM-16:00PM Coordinated Universal Time (UTC)
Wednesday, 17 August 2022 | Course runs 2 consecutive days, 2 hours per day
10:00AM-12:00PM Eastern Daylight Time (EDT)
09:00AM-11:00AM Central Daylight Time (CDT)
14:00PM-16:00PM Coordinated Universal Time (UTC)
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DESCRIPTION
In this course, we will cover the steps and decisions involved in calibrating a mathematical model in R. We will begin the course with an overview of when model calibration is necessary and will introduce a general model calibration framework. We will then engage students in an extensive hands-on exercise where they will implement the calibration of a simple mathematical model in R using a simple random search. We will then introduce more advanced calibration approaches, including Latin hypercube sampling, directed search algorithms (eg, Nelder-Mead), Bayesian calibration, and other iterative calibration approaches (eg, genetic algorithms). We will discuss the tradeoffs of different calibration approaches and will identify scenarios when one approach may be more appropriate than others.
Participants who wish to gain hands-on experience are required to bring their laptops with R and RStudio installed.
Eva A. Enns, PhD, MS
Associate Professor
Division of Health Policy and Management
University of Minnesota School of Public Health
Minneapolis, MN, USA
Fernando Alarid-Escudero, PhD
Assistant Professor
Drug Policy Program
Center for Research and Teaching in Economics (CIDE)
Aguascalientes, Mexico
Basic Schedule:
4 Hours | Course runs 2 consecutive days, 2 hours each day