Combining quantitative data with logic-based specifications for parameter inference

Paul  Piho; Jane Hillston

doi:10.1007/978-3-031-16011-0_9

Combining quantitative data with logic-based specifications for parameter inference

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract / Description of output

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 qualitative 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 qualitative knowledge into one parameter inference problem via rejection sampling.

Original language	English
Title of host publication	From Data to Models and Back: 10th International Symposium, DataMod 2021, Virtual Event, December 6–7, 2021, Revised Selected Papers
Editors	Juliana Bowles, Giovanna Broccia, Roberto Pellungrini
Publisher	Springer
Pages	121-137
Number of pages	16
ISBN (Electronic)	978-3-031-16011-0
ISBN (Print)	978-3-031-16010-3
DOIs	https://doi.org/10.1007/978-3-031-16011-0_9
Publication status	Published - 15 Oct 2022
Event	10th International Symposium From Data to Models and Back - Duration: 6 Dec 2021 → 7 Dec 2021 Conference number: 10 https://datamod2021.github.io/index.html

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Cham
Volume	13268
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Symposium

Symposium	10th International Symposium From Data to Models and Back
Abbreviated title	DataMod 2021
Period	6/12/21 → 7/12/21
Internet address	https://datamod2021.github.io/index.html

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Combining quantitative data_PIHO_DOA05122021_AFVAccepted author manuscript, 2.18 MBLicence: All Rights Reserved

https://link.springer.com/chapter/10.1007/978-3-031-16011-0_9Licence: All Rights Reserved

Cite this

Piho, P., & Hillston, J. (2022). Combining quantitative data with logic-based specifications for parameter inference. In J. Bowles, G. Broccia, & R. Pellungrini (Eds.), From Data to Models and Back: 10th International Symposium, DataMod 2021, Virtual Event, December 6–7, 2021, Revised Selected Papers (pp. 121-137). (Lecture Notes in Computer Science; Vol. 13268). Springer. https://doi.org/10.1007/978-3-031-16011-0_9

Piho, Paul ; Hillston, Jane. / Combining quantitative data with logic-based specifications for parameter inference. From Data to Models and Back: 10th International Symposium, DataMod 2021, Virtual Event, December 6–7, 2021, Revised Selected Papers. editor / Juliana Bowles ; Giovanna Broccia ; Roberto Pellungrini. Springer, 2022. pp. 121-137 (Lecture Notes in Computer Science).

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title = "Combining quantitative data with logic-based specifications for parameter inference",

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 qualitative 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 qualitative knowledge into one parameter inference problem via rejection sampling.",

author = "Paul Piho and Jane Hillston",

note = "Publisher Copyright: {\textcopyright} 2022, Springer Nature Switzerland AG.; 10th International Symposium From Data to Models and Back, DataMod 2021 ; Conference date: 06-12-2021 Through 07-12-2021",

year = "2022",

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booktitle = "From Data to Models and Back: 10th International Symposium, DataMod 2021, Virtual Event, December 6–7, 2021, Revised Selected Papers",

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Piho, P & Hillston, J 2022, Combining quantitative data with logic-based specifications for parameter inference. in J Bowles, G Broccia & R Pellungrini (eds), From Data to Models and Back: 10th International Symposium, DataMod 2021, Virtual Event, December 6–7, 2021, Revised Selected Papers. Lecture Notes in Computer Science, vol. 13268, Springer, pp. 121-137, 10th International Symposium From Data to Models and Back, 6/12/21. https://doi.org/10.1007/978-3-031-16011-0_9

Combining quantitative data with logic-based specifications for parameter inference. / Piho, Paul ; Hillston, Jane.
From Data to Models and Back: 10th International Symposium, DataMod 2021, Virtual Event, December 6–7, 2021, Revised Selected Papers. ed. / Juliana Bowles; Giovanna Broccia; Roberto Pellungrini. Springer, 2022. p. 121-137 (Lecture Notes in Computer Science; Vol. 13268).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Combining quantitative data with logic-based specifications for parameter inference

AU - Piho, Paul

AU - Hillston, Jane

N1 - Conference code: 10

PY - 2022/10/15

Y1 - 2022/10/15

N2 - 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 qualitative 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 qualitative knowledge into one parameter inference problem via rejection sampling.

AB - 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 qualitative 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 qualitative knowledge into one parameter inference problem via rejection sampling.

U2 - 10.1007/978-3-031-16011-0_9

DO - 10.1007/978-3-031-16011-0_9

M3 - Conference contribution

SN - 978-3-031-16010-3

T3 - Lecture Notes in Computer Science

SP - 121

EP - 137

BT - From Data to Models and Back: 10th International Symposium, DataMod 2021, Virtual Event, December 6–7, 2021, Revised Selected Papers

A2 - Bowles, Juliana

A2 - Broccia, Giovanna

A2 - Pellungrini, Roberto

PB - Springer

T2 - 10th International Symposium From Data to Models and Back

Y2 - 6 December 2021 through 7 December 2021

ER -

Piho P, Hillston J. Combining quantitative data with logic-based specifications for parameter inference. In Bowles J, Broccia G, Pellungrini R, editors, From Data to Models and Back: 10th International Symposium, DataMod 2021, Virtual Event, December 6–7, 2021, Revised Selected Papers. Springer. 2022. p. 121-137. (Lecture Notes in Computer Science). doi: 10.1007/978-3-031-16011-0_9

Combining quantitative data with logic-based specifications for parameter inference

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