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Lecture 3: Assessing model performance
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2023 AARMS-EIDM Summer School, Lecture 3 "Assessing model performance" by James Watmough as part of Course 2. Data, models, and decision support.
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EIDM ☜The Emerging Infectious Diseases Modelling Initiative (EIDM) – by the Public Health Agency of Canada and NSERC – aims to establish multi-disciplinary network(s) of specialists across the country in modelling infectious diseases to be applied to public needs associated with emerging infectious diseases and pandemics such as COVID-19. [1]☜F1CEB7
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23/08/19 AARMS-EIDM Summer School on Modelling Infectious Diseases »
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2023 Summer School in EIDM Course 2: Data, models and decision support »
2023 Summer School in EIDM Course 2: Data, models and decision support
2023 Summer School in EIDM Course 2: Data, models and decision support☜This course teaches the technical skills to parameterize dynamical systems models in ecology and evolution and quantify prediction uncertainty. The course examines case studies to understand the use of models in decision support.☜E883B6
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Lecture 3: Assessing model performance
Lecture 3: Assessing model performance☜2023 AARMS-EIDM Summer School, Lecture 3 Assessing model performance by James Watmough as part of Course 2. Data, models, and decision support.☜E883B6
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James Watmough »
James Watmough
James Watmough☜Professor in the Department of Mathematics and Statistics at the University of New Brunswick.☜FFFACD
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Lecture 3: Assessing model performance - Slides
Author:
James Watmough
Publication date:
26 August 2023
Cited by:
David Price
3:56 PM 18 February 2024 GMT
URL:
https://debategraph.org/handler.ashx?path=ROOT%2fu2928%2fBBSchool09-assessing-model-performance.pdf&att=1
Excerpt / Summary
Overview:
(1) Holistic vs reductionist
• keep in mind what your model is assuming and what it neglects
• it’s the things you didn’t know you didn’t know that stab you in the back
(2) Model assessment is a testing of model assumptions
• hypothesis testing involves model comparisons, not comparisons between models and reality
• creative visualizations that best illustrate assessment
• relevant statistics
• avoid just listing and tallying scores
• descriptive vs inferential statistics
(3) Identify structural errors in models
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