J 2023

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

KLEIN, Julia; Huy PHUNG; Matej HAJNAL; David ŠAFRÁNEK; Tatjana PETROV et. al.

Základní údaje

Originální název

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

Autoři

KLEIN, Julia; Huy PHUNG; Matej HAJNAL (703 Slovensko, domácí); David ŠAFRÁNEK (203 Česká republika, domácí) a Tatjana PETROV

Vydání

Plos one, San Francisco, Public Library of Science, 2023, 1932-6203

Další údaje

Jazyk

angličtina

Typ výsledku

Článek v odborném periodiku

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Spojené státy

Utajení

není předmětem státního či obchodního tajemství

Odkazy

Impakt faktor

Impact factor: 2.900

Kód RIV

RIV/00216224:14330/23:00134426

Organizační jednotka

Fakulta informatiky

UT WoS

001125277400020

EID Scopus

2-s2.0-85176771172

Klíčová slova anglicky

Bayes Theorem; Markov Chains

Příznaky

Mezinárodní význam, Recenzováno
Změněno: 8. 4. 2024 06:19, RNDr. Pavel Šmerk, Ph.D.

Anotace

V originále

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.

Návaznosti

GA22-10845S, projekt VaV
Název: Studium role polyhydroxyalkanoátů u bakterie Schlegelella thermodepolymerans – slibného bakteriálního kandidáta pro biotechnologie nové generace (Akronym: PHAST)
Investor: Grantová agentura ČR, Studium role polyhydroxyalkanoátů u bakterie Schlegelella thermodepolymerans – slibného bakteriálního kandidáta pro biotechnologie nové generace