Data-Informed Parameter Synthesis for Population Markov Chains

  • Matej Hajnal
  • Morgane Nouvian
  • David Šafránek
  • Tatjana PetrovEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11705)


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.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Matej Hajnal
    • 2
    • 4
  • Morgane Nouvian
    • 1
    • 3
  • David Šafránek
    • 4
  • Tatjana Petrov
    • 2
    • 3
    Email author
  1. 1.Department of BiologyUniversity of KonstanzKonstanzGermany
  2. 2.Department of Computer and Information ScienceUniversity of KonstanzKonstanzGermany
  3. 3.Centre for the Advanced Study of Collective BehaviourUniversity of KonstanzKonstanzGermany
  4. 4.Systems Biology Laboratory, Faculty of InformaticsMasaryk UniversityBrnoCzech Republic

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