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@inproceedings{1648999,
author = {Hajnal, Matej and Nouvian, Morgan and Šafránek, David and Petrov, Tatjana},
address = {Cham},
booktitle = {Hybrid Systems Biology (HSB 2019)},
doi = {http://dx.doi.org/10.1007/978-3-030-28042-0_10},
edition = {LNCS 11705},
editor = {Ceska, M et al.},
keywords = {Stochastic population models; Markov processes; Parameter synthesis},
howpublished = {tištěná verze "print"},
language = {eng},
location = {Cham},
isbn = {978-3-030-28041-3},
pages = {147-164},
publisher = {Springer International Publishing},
title = {Data-Informed Parameter Synthesis for Population Markov Chains},
year = {2019}
}
TY - JOUR
ID - 1648999
AU - Hajnal, Matej - Nouvian, Morgan - Šafránek, David - Petrov, Tatjana
PY - 2019
TI - Data-Informed Parameter Synthesis for Population Markov Chains
PB - Springer International Publishing
CY - Cham
SN - 9783030280413
KW - Stochastic population models
KW - Markov processes
KW - Parameter synthesis
N2 - Stochastic population models are widely used to model phenomena in different areas such as chemical kinetics or collective animal behaviour. Quantitative analysis of stochastic population models easily becomes challenging, due to the combinatorial propagation of dependencies across the population. The complexity becomes especially prominent when model's parameters are not known and available measurements are limited. In this paper, we illustrate this challenge in a concrete scenario: we assume a simple communication scheme among identical individuals, inspired by how social honeybees emit the alarm pheromone to protect the colony in case of danger. Together, n individuals induce a population Markov chain with n parameters. In addition, we assume to be able to experimentally observe the states only after the steady-state is reached. In order to obtain the parameters of the individual's behaviour, by utilising the data measurements for population, we combine two existing techniques. First, we use the tools for parameter synthesis for Markov chains with respect to temporal logic properties, and then we employ CEGAR-like reasoning to find the viable parameter space up to desired coverage. We report the performance on a number of synthetic data sets.
ER -
HAJNAL, Matej, Morgan NOUVIAN, David ŠAFRÁNEK a Tatjana PETROV. Data-Informed Parameter Synthesis for Population Markov Chains. In Ceska, M et al. \textit{Hybrid Systems Biology (HSB 2019)}. LNCS 11705. Cham: Springer International Publishing, 2019, s.~147-164. ISBN~978-3-030-28041-3. Dostupné z: https://dx.doi.org/10.1007/978-3-030-28042-0\_{}10.
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