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SUMMARY:Variance reduction approaches to efficient simulation of biochemic
al reaction network models
DTSTART;VALUE=DATE-TIME:20180712T052000Z
DTEND;VALUE=DATE-TIME:20180712T054000Z
DTSTAMP;VALUE=DATE-TIME:20220819T210124Z
UID:indico-contribution-325@conferences.maths.unsw.edu.au
DESCRIPTION:Speakers: Ruth Baker (University of Oxford)\nThe complexity of
biochemical reaction networks means that we often rely on stochastic simu
lation to investigate their potential behaviours\, generating multiple sam
ple paths from the model and using them to estimate summary statistics of
interest. However\, for realistic models\, existing Monte Carlo methods ar
e often prohibitive when it comes to exploring the range of possible model
behaviours\, conducting parameter sensitivity analysis\, or parameter inf
erence and model selection. \n\nOne approach that has the potential to mit
igate these issues is that of variance reduction. Instead of attempting to
reduce the computational cost of generating individual sample paths\, we
instead aim to reduce the variability in the summary statistics we are int
erested in. Computational savings can then be made because fewer sample pa
ths are required to generate estimates to within a specified error. In thi
s talk I will describe variance reduction approaches biochemical reaction
network models. I will first show how point estimates can be generated sim
ply and efficiently\, and then outline how extensions to the basic method
allow variance reduction approaches to be applied to a range of problems a
nd summary statistics.\n\nhttps://conferences.maths.unsw.edu.au/event/2/co
ntributions/325/
LOCATION:University of Sydney New Law School/--102
URL:https://conferences.maths.unsw.edu.au/event/2/contributions/325/
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