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X-ORIGINAL-URL:https://www.ibs.re.kr/bimag
X-WR-CALDESC:Events for Biomedical Mathematics Group
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TZID:Asia/Seoul
BEGIN:STANDARD
TZOFFSETFROM:+0900
TZOFFSETTO:+0900
TZNAME:KST
DTSTART:20220101T000000
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BEGIN:VEVENT
DTSTART;TZID=Asia/Seoul:20231110T110000
DTEND;TZID=Asia/Seoul:20231110T120000
DTSTAMP:20260528T204603
CREATED:20230831T142922Z
LAST-MODIFIED:20240728T144105Z
UID:8399-1699614000-1699617600@www.ibs.re.kr
SUMMARY:Matthew Simpson\, Efficient prediction\, estimation and identifiability analysis with mechanistic mathematical models
DESCRIPTION:Abstract: Interpreting data using mechanistic mathematical models provides a foundation for discovery and decision-making in all areas of science and engineering. Key steps in using mechanistic mathematical models to interpret data include: (i) identifiability analysis; (ii) parameter estimation; and (iii) model prediction. Here we present a systematic\, computationally efficient likelihood-based workflow that addresses all three steps in a unified way. Recently developed methods for constructing profile-wise prediction intervals enable this workflow and provide the central linkage between different workflow components. These methods propagate profile-likelihood-based confidence sets for model parameters to predictions in a way that isolates how different parameter combinations affect model predictions. We show how to extend these profile-wise prediction intervals to two-dimensional interest parameters\, and then combine profile-wise prediction confidence sets to give an overall prediction confidence set that approximates the full likelihood-based prediction confidence set well. We apply our methods to a range of synthetic data and real-world ecological data describing re-growth of coral reefs on the Great Barrier Reef after some external disturbance\, such as a tropical cyclone or coral bleaching event.
URL:https://www.ibs.re.kr/bimag/event/matthew-simpson-efficient-prediction-estimation-and-identifiability-analysis-with-mechanistic-mathematical-models/
LOCATION:ZOOM ID: 997 8258 4700 (Biomedical Mathematics Online Colloquium)\, (pw: 1234)
CATEGORIES:Biomedical Mathematics Online Colloquium
ATTACH;FMTTYPE=image/jpeg:https://www.ibs.re.kr/bimag/cms/wp-content/uploads/2023/08/Matthew-Simpson-e1722177652995.jpeg
ORGANIZER;CN="Jae Kyoung Kim":MAILTO:jaekkim@kaist.ac.kr
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