Data-Informed Parameter Synthesis for Population Markov Chains
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Population models are widely used to model different phenomena: animal collectives such as social insects, flocking birds, schooling fish, or humans within societies, as well as molecular species inside a cell, cells forming a tissue. Animal collectives show remarkable self-organisation towards emergent behaviours without centralised control. Quantitative models of the underlying mechanisms can directly serve important societal concerns (for example, prediction of seismic activity [5]), inspire the design of distributed algorithms (for example, ant colony algorithm [1]), or aid robust design and engineering of collective, adaptive systems under given functionality and resources, which is recently gaining attention in vision of smart cities [3, 4]. Quantitative prediction of the behaviour of a population of agents over time and space, each having several behavioural modes, results in a high-dimensional, non-linear, and stochastic system [2]. Hence, computational modelling with population models is challenging, especially when the model parameters are unknown and experiments are expensive.
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HAJNAL, Matej, Morgane NOUVIAN, Tatjana PETROV, David SAFRANEK, 2019. Data-Informed Parameter Synthesis for Population Markov Chains. 17th international conference, CMSB 2019. Trieste, Italy, 18. Sept. 2019 - 20. Sept. 2019. In: BORTOLUSSI, Luca, ed., Guido SANGUINETTI, ed.. Computational methods in systems biology : 17th international conference, CMSB 2019, Trieste, Italy, September 18-20, 2019 : proceedings. Cham: Springer, 2019, pp. 383-386. Lecture Notes in Computer Science / Lecture notes in bioinformatics. 11773. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-030-31303-6. Available under: doi: 10.1007/978-3-030-31304-3_32BibTex
@inproceedings{Hajnal2019-09-17DataI-50675, year={2019}, doi={10.1007/978-3-030-31304-3_32}, title={Data-Informed Parameter Synthesis for Population Markov Chains}, number={11773}, isbn={978-3-030-31303-6}, issn={0302-9743}, publisher={Springer}, address={Cham}, series={Lecture Notes in Computer Science / Lecture notes in bioinformatics}, booktitle={Computational methods in systems biology : 17th international conference, CMSB 2019, Trieste, Italy, September 18-20, 2019 : proceedings}, pages={383--386}, editor={Bortolussi, Luca and Sanguinetti, Guido}, author={Hajnal, Matej and Nouvian, Morgane and Petrov, Tatjana and Safranek, David} }
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