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Dynamical inference of hidden biological populations.

Research output: Contribution to journalJournal article


<mark>Journal publication date</mark>10/2008
<mark>Journal</mark>European Physical Journal B
Issue number3
Number of pages9
Pages (from-to)369-377
<mark>Original language</mark>English


Population fluctuations in a predator-prey system are analyzed for the case where the number of prey could be determined, subject to measurement noise, but the number of predators was unknown. The problem of how to infer the unmeasured predator dynamics, as well as the model parameters, is addressed. Two solutions are suggested. In the first of these, measurement noise and the dynamical noise in the equation for predator population are neglected; the problem is reduced to a one-dimensional case, and a Bayesian dynamical inference algorithm is employed to reconstruct the model parameters. In the second solution a full-scale Markov Chain Monte Carlo simulation is used to infer both the unknown predator trajectory, and also the model parameters, using the one-dimensional solution as an initial guess.

Bibliographic note

The final publication is available at Springer via http://dx.doi.org/10.1140/epjb/e2008-00340-5