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Dynamical inference of hidden biological populations.

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Dynamical inference of hidden biological populations. / Luchinsky, Dmitri G.; Smelyanskiy, V. N.; Millonas, M. et al.
In: European Physical Journal B, Vol. 65, No. 3, 10.2008, p. 369-377.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Luchinsky, DG, Smelyanskiy, VN, Millonas, M & McClintock, PVE 2008, 'Dynamical inference of hidden biological populations.', European Physical Journal B, vol. 65, no. 3, pp. 369-377. https://doi.org/10.1140/epjb/e2008-00340-5

APA

Luchinsky, D. G., Smelyanskiy, V. N., Millonas, M., & McClintock, P. V. E. (2008). Dynamical inference of hidden biological populations. European Physical Journal B, 65(3), 369-377. https://doi.org/10.1140/epjb/e2008-00340-5

Vancouver

Luchinsky DG, Smelyanskiy VN, Millonas M, McClintock PVE. Dynamical inference of hidden biological populations. European Physical Journal B. 2008 Oct;65(3):369-377. doi: 10.1140/epjb/e2008-00340-5

Author

Luchinsky, Dmitri G. ; Smelyanskiy, V. N. ; Millonas, M. et al. / Dynamical inference of hidden biological populations. In: European Physical Journal B. 2008 ; Vol. 65, No. 3. pp. 369-377.

Bibtex

@article{9907957ca8a2424094c43e0498156493,
title = "Dynamical inference of hidden biological populations.",
abstract = "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.",
keywords = "PACS. 02.50.Tt Inference methods – 02.50.Ng Distribution theory and Monte Carlo studies – 87.23.Cc Population dynamics and ecological pattern formation – 02.50.-r Probability theory, stochastic processes, and statistics",
author = "Luchinsky, {Dmitri G.} and Smelyanskiy, {V. N.} and M. Millonas and McClintock, {Peter V. E.}",
note = "The final publication is available at Springer via http://dx.doi.org/10.1140/epjb/e2008-00340-5",
year = "2008",
month = oct,
doi = "10.1140/epjb/e2008-00340-5",
language = "English",
volume = "65",
pages = "369--377",
journal = "European Physical Journal B",
issn = "1434-6028",
publisher = "Springer New York LLC",
number = "3",

}

RIS

TY - JOUR

T1 - Dynamical inference of hidden biological populations.

AU - Luchinsky, Dmitri G.

AU - Smelyanskiy, V. N.

AU - Millonas, M.

AU - McClintock, Peter V. E.

N1 - The final publication is available at Springer via http://dx.doi.org/10.1140/epjb/e2008-00340-5

PY - 2008/10

Y1 - 2008/10

N2 - 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.

AB - 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.

KW - PACS. 02.50.Tt Inference methods – 02.50.Ng Distribution theory and Monte Carlo studies – 87.23.Cc Population dynamics and ecological pattern formation – 02.50.-r Probability theory

KW - stochastic processes

KW - and statistics

U2 - 10.1140/epjb/e2008-00340-5

DO - 10.1140/epjb/e2008-00340-5

M3 - Journal article

VL - 65

SP - 369

EP - 377

JO - European Physical Journal B

JF - European Physical Journal B

SN - 1434-6028

IS - 3

ER -