ABC(SMC)$$^2$$: Simultaneous Inference and Model Checking of Chemical Reaction Networks

Molyneux, Gareth W.; Abate, Alessandro

doi:10.1007/978-3-030-60327-4_14

Cited by 4 publications

(1 citation statement)

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“…Given a parameter candidate ŷ that specifies a model M y , the ABC algorithm accepts ŷ if a simulation run on M y delivers observable data D obs such that δ(D obs , D sim ) < �, where � 2 R �0 is the distance threshold. ABC algorithm can be used together with Markov chain Monte Carlo algorithms (ABC-MCMC [44,45]), or with Sequential Monte Carlo sampling algorithms (ABC-SMC [46,47]). We implement the latter.…”

Section: Likelihood-free Sampling-based Inferencementioning

confidence: 99%

Combining formal methods and Bayesian approach for inferring discrete-state stochastic models from steady-state data

Klein,

Phung,

Hajnal

et al. 2023

PLoS ONE

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Stochastic population models are widely used to model phenomena in different areas such as cyber-physical systems, chemical kinetics, collective animal behaviour, and beyond. Quantitative analysis of stochastic population models easily becomes challenging due to the combinatorial number of possible states of the population. Moreover, while the modeller easily hypothesises the mechanistic aspects of the model, the quantitative parameters associated to these mechanistic transitions are difficult or impossible to measure directly. In this paper, we investigate how formal verification methods can aid parameter inference for population discrete-time Markov chains in a scenario where only a limited sample of population-level data measurements—sample distributions among terminal states—are available. We first discuss the parameter identifiability and uncertainty quantification in this setup, as well as how the existing techniques of formal parameter synthesis and Bayesian inference apply. Then, we propose and implement four different methods, three of which incorporate formal parameter synthesis as a pre-computation step. We empirically evaluate the performance of the proposed methods over four representative case studies. We find that our proposed methods incorporating formal parameter synthesis as a pre-computation step allow us to significantly enhance the accuracy, precision, and scalability of inference. Specifically, in the case of unidentifiable parameters, we accurately capture the subspace of parameters which is data-compliant at a desired confidence level.

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