University of Cambridge > > Networks & Neuroscience > Inference for stochastic models

Inference for stochastic models

Add to your list(s) Download to your calendar using vCal

If you have a question about this talk, please contact Duncan Simpson.

Mathematical models are able to capture the essence of dynamic biological processes. There is still a gap between the precision and detail of mathematical models and the limitations of experimental data. Flow cytometry techniques, for instance, provide data on gene regulation in single cells but usually only on very few variables. Many of the processes responsible for variety between cells are unobserved and are best modelled by general noise terms. Noise due to fluctuation on the molecular level might also play a role. That is, simplification of models and explicit modelling of noise is required if such models are fitted to currently available data.

This talk is part of the Networks & Neuroscience series.

Tell a friend about this talk:

This talk is included in these lists:

Note that ex-directory lists are not shown.


© 2006-2023, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity