Combining quantitative data with logic-based specifications for parameter inference

Paul Piho, Jane Hillston

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publicationProceedings of DataMod 2021 (10th International Symposium From Data to Models and Back)
Number of pages16
Publication statusAccepted/In press - 20 Nov 2021
Event10th International Symposium From Data to Models and Back -
Duration: 6 Dec 20217 Dec 2021
Conference number: 10
https://datamod2021.github.io/index.html

Symposium

Symposium10th International Symposium From Data to Models and Back
Abbreviated titleDataMod 2021
Period6/12/217/12/21
Internet address

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