Data-informed parameter synthesis for population Markov chains
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 protocols 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 on 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 model 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 available data, and without making any further modelling assumption, 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.
Sun 7 AprDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
16:00 - 18:00 | |||
16:00 30mTalk | Extracting landscape features from single particle trajectories HSB Ádám Halász West Virginia University, Ouri Maler West Virginia University, Jeremy S Edwards University of New Mexico | ||
16:30 30mTalk | Fuzzy Matching in Symbolic Systems Biology HSB Adrian Riesco Universidad Complutense de Madrid, Beatriz Santos-Buitrago Seoul National University, Merrill Knapp SRI International, Gustavo Santos-Garcia Universidad de Salamanca, Carolyn Talcott SRI International | ||
17:00 30mTalk | Data-informed parameter synthesis for population Markov chains HSB Matej Hajnal Masaryk University, Tatjana Petrov Universität Konstanz, David Safranek Masaryk University, Morgane Nouvian University of Konstanz | ||
17:30 10mDay closing | Closing HSB |