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DESCRIPTION:Qualitative inverse problems: Mapping from limited data to prop
 erties of dynamics and parameter values for ODE models\n\nJonathan Rubin (
 University of Pittsburgh)\n	Tuesday November 21\, 12-1pm\n	Zoom Link: https:
 //mcgill.zoom.us/j/86855481591\n	In Person: 550 Sherbrooke\, Room 189\n	\n	Ab
 stract: Questions of parameter estimation – that is\, finding the paramete
 r values that allow a model to best fit some data – and parameter identifi
 ability – that is\, the uniqueness of such parameter values – are often co
 nsidered in settings where experiments can be repeated to gain more certai
 nty about the data. In this talk\, however\, I will consider parameter est
 imation and parameter identifiability in situations where available data c
 onsists of measurements from discrete time points during a single experime
 nt. Our motivation comes from medical settings\, where data comes from a p
 atient\; such limitations in data also arise in finance\, ecology\, and cl
 imate\, for example. In this setting\, we can try to find the best paramet
 ers to fit our limited data. In this talk\, I will introduce a novel\, alt
 ernative goal\, which we refer to as a qualitative inverse problem. The ai
 m here is to analyze what information we can gain about a system from the 
 available data even if we cannot estimate its parameter values precisely. 
 I will discuss results that allow us to determine whether a given model ha
 s the ability to fit the data\, whether its parameters are identifiable\, 
 the signs of model parameters\, and/or the local dynamics around system fi
 xed points\, as well as how much measurement error can be tolerated withou
 t changing the conclusions of our analysis. I will consider various classe
 s of model systems and will illustrate our latest results with the classic
  Lotka-Volterra system.\n
DTSTART:20231121T170000Z
DTEND:20231121T180000Z
SUMMARY:QLS Seminar Series - Jonathan Rubin
URL:/qls/channels/event/qls-seminar-series-jonathan-ru
 bin-352196
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