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SUMMARY:Challenges in using maximum-likelihood inference for multi-dimensi
 onal stochastic models: the case of within-host dynamics of Salmonella - O
 livier Restif (Dept. Vet. Med.)
DTSTART:20130304T113000Z
DTEND:20130304T123000Z
UID:TALK41114@talks.cam.ac.uk
CONTACT:Prof. Julia Gog
DESCRIPTION:Progress in experimental techniques provides new insight into 
 the dynamics of infection within hosts. In order to make inference on the 
 processes driving the pathogen's population dynamics and assess the validi
 ty of biological hypotheses\, it is necessary to design mechanistic mathem
 atical models which can be fitted to the data. The objectives are usually 
 to compare alternative models and estimate the parameters of the "best-fit
 ting model(s)". The last few years have seen rapid development in the stat
 istical frameworks available\, but there remain issues with their ability 
 to deal with either complex stochastic models or with uncertainty in the d
 ata collection process. I will present my ongoing attempt to address these
  two issues within a classic maximum-likelihood framework\, in the hope th
 at it can be improved or complemented by some state-of-the-art Bayesian al
 gorithms.
LOCATION:DD48\, Cripps Court\, Queens' College
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