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SUMMARY:Identifiability of Stochastic and Spatial Models in Mathematical B
 iology - Alex Browning (University of Melbourne)
DTSTART;VALUE=DATE-TIME:20260306T110000Z
DTEND;VALUE=DATE-TIME:20260306T120000Z
UID:https://talks.ox.ac.uk/talks/id/607331ef-aa29-4e8d-8998-aa901824ae36/
DESCRIPTION:Effective application of mathematical models to interpret biol
 ogical data and make accurate predictions often requires that model parame
 ters are identifiable. Requisite to identifiability from a finite amount o
 f noisy data is that model parameters are first structurally identifiable:
  a mathematical question that establishes whether multiple parameter value
 s may give rise to indistinguishable model outputs. Approaches to assess s
 tructural identifiability of deterministic ordinary differential equation 
 models are well-established\, however tools for the assessment of the incr
 easingly relevant stochastic and spatial models remain in their infancy. \
 n\nI provide in this talk an introduction to structural identifiability\, 
 before presenting new frameworks for the assessment of stochastic and part
 ial differential equations. Importantly\, I discuss the relevance of our m
 ethodology to model selection\, and more the practical and aptly named pra
 ctical identifiability of parameters in the context of experimental data. 
 Finally\, I conclude with a brief discussion of future research directions
  and remaining open questions.\nSpeakers:\nAlex Browning (University of Me
 lbourne)
LOCATION:Mathematical Institute (L4)\, Woodstock Road OX2 6GG
TZID:Europe/London
URL:https://talks.ox.ac.uk/talks/id/607331ef-aa29-4e8d-8998-aa901824ae36/
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DESCRIPTION:Talk:Identifiability of Stochastic and Spatial Models in Mathe
 matical Biology - Alex Browning (University of Melbourne)
TRIGGER:-PT1H
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