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Combining Quantitative Data with Logic-Based Specifications for Parameter Inference

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From Data to Models and Back (DataMod 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13268))

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Abstract

Continuous time Markov chains are a common mathematical model for a range of natural and computer systems. An important part of constructing such models is fitting the model parameters based on some observed data or prior domain knowledge. In this paper we consider the problem of fitting model parameters with respect to a mix of quantitative data and data formulated as temporal logic formulae. Our approach works by defining a set of conditions that capture the dynamics inferred by the quantitative data. This allows for a straightforward way to combine the information from the quantitative and logically specified knowledge into one parameter inference problem via rejection sampling.

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Authors and Affiliations

  1. University of Edinburgh, Edinburgh, UK

    Paul Piho & Jane Hillston

Authors
  1. Paul Piho
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  2. Jane Hillston
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Correspondence to Paul Piho .

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Editors and Affiliations

  1. University of St Andrews, St Andrews, UK

    Juliana Bowles

  2. ISTI-CNR, Pisa, Italy

    Giovanna Broccia

  3. University of Pisa, Pisa, Italy

    Roberto Pellungrini

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Piho, P., Hillston, J. (2022). Combining Quantitative Data with Logic-Based Specifications for Parameter Inference. In: Bowles, J., Broccia, G., Pellungrini, R. (eds) From Data to Models and Back. DataMod 2021. Lecture Notes in Computer Science, vol 13268. Springer, Cham. https://doi.org/10.1007/978-3-031-16011-0_9

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  • DOI: https://doi.org/10.1007/978-3-031-16011-0_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16010-3

  • Online ISBN: 978-3-031-16011-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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