Data-Informed Parameter Synthesis for Population Markov Chains

Hajnal, Matej; Nouvian, Morgane; Šafránek, David; Petrov, Tatjana

doi:10.1007/978-3-030-28042-0_10

Cited by 9 publications

(16 citation statements)

References 33 publications

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“…We evaluated fPMC for three software systems and processes taken from related research [4], [5], [25], [27], [28] and belonging to different application domains. We selected these systems because (i) their Markov models include hyperparameters that can be tuned to devise pDTMCs with various numbers of states S and transition probability matrices P; and (ii) without changing the pDTMC structure, several nontrivial transition probabilities in P can become parameters, thus increasing the number of parameters within the model.…”

Section: B Experimental Setupmentioning

confidence: 99%

“…We selected these systems because (i) their Markov models include hyperparameters that can be tuned to devise pDTMCs with various numbers of states S and transition probability matrices P; and (ii) without changing the pDTMC structure, several nontrivial transition probabilities in P can become parameters, thus increasing the number of parameters within the model. Due to space constraints, we only provide brief descriptions of these systems; full details are available from [4], [5], [25], [27], [28].…”

Section: B Experimental Setupmentioning

confidence: 99%

“…COM Process. We consider a communication (COM) process [27] among n identical individuals, inspired by the way that honeybees emit an alarm pheromone to recruit workers and protect their colonies from intruders. Due to the selfdestructive defence behaviour in social insects, the recruited workers die after completing their defence actions.…”

Section: B Experimental Setupmentioning

confidence: 99%

“…We limit construct validity threats that may originate from simplifications and assumptions made when establishing the experimental methodology using the pDTMCs and reachability properties from three publicly-available software systems and processes, also used in related research [4], [5], [25], [27], [28].…”

Section: Threats To Validitymentioning

confidence: 99%

“…Parametric model checking of reachability properties P 1,...,P 21 from[27] for the COM process with n = 20 individuals (parameters) and α = 50, showing the execution time and the number of arithmetic operations of the derived formulae. The pDTMC has 234 states and 444 transitions.…”

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confidence: 99%

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Fast Parametric Model Checking through Model Fragmentation

Fang

Călinescu

Gerasimou

et al. 2021

Preprint

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Parametric model checking (PMC) computes algebraic formulae that express key non-functional properties of a system (reliability, performance, etc.) as rational functions of the system and environment parameters. In software engineering, PMC formulae can be used during design, e.g., to analyse the sensitivity of different system architectures to parametric variability, or to find optimal system configurations. They can also be used at runtime, e.g., to check if non-functional requirements are still satisfied after environmental changes, or to select new configurations after such changes. However, current PMC techniques do not scale well to systems with complex behaviour and more than a few parameters. Our paper introduces a fast PMC (fPMC) approach that overcomes this limitation, extending the applicability of PMC to a broader class of systems than previously possible. To this end, fPMC partitions the Markov models that PMC operates with into fragments whose reachability properties are analysed independently, and obtains PMC reachability formulae by combining the results of these fragment analyses. To demonstrate the effectiveness of fPMC, we show how our fPMC tool can analyse three systems (taken from the research literature, and belonging to different application domains) with which current PMC techniques and tools struggle.

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Section: B Experimental Setupmentioning

confidence: 99%

Section: B Experimental Setupmentioning

confidence: 99%

Section: B Experimental Setupmentioning

confidence: 99%

Section: Threats To Validitymentioning

confidence: 99%

mentioning

confidence: 99%

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Fast Parametric Model Checking through Model Fragmentation

Fang

Călinescu

Gerasimou

et al. 2021

Preprint

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StochNetV2: A Tool for Automated Deep Abstractions for Stochastic Reaction Networks

Repin

Phung

Petrov

2020

Lecture Notes in Computer Science

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Analysis of Markov Jump Processes under Terminal Constraints

Backenköhler

Bortolussi

Großmann

et al. 2021

Tools and Algorithms for the Construction and Analysis of Systems

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Many probabilistic inference problems such as stochastic filtering or the computation of rare event probabilities require model analysis under initial and terminal constraints. We propose a solution to this bridging problem for the widely used class of population-structured Markov jump processes. The method is based on a state-space lumping scheme that aggregates states in a grid structure. The resulting approximate bridging distribution is used to iteratively refine relevant and truncate irrelevant parts of the state-space. This way, the algorithm learns a well-justified finite-state projection yielding guaranteed lower bounds for the system behavior under endpoint constraints. We demonstrate the method’s applicability to a wide range of problems such as Bayesian inference and the analysis of rare events.

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Data-Informed Parameter Synthesis for Population Markov Chains

Cited by 9 publications

References 33 publications

Fast Parametric Model Checking through Model Fragmentation

Fast Parametric Model Checking through Model Fragmentation

StochNetV2: A Tool for Automated Deep Abstractions for Stochastic Reaction Networks

Analysis of Markov Jump Processes under Terminal Constraints

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