Reilly, D, Taylor, MJ, Fergus, P, Chalmers, C and Thompson, S (2021) Misper-Bayes: A Bayesian Network Model for Missing Person Investigations. IEEE Access, 9. pp. 49990-50000. ISSN 2169-3536

Preview

Text Misper-Bayes_A_Bayesian_Network_Model_for_Missing_Person_Investigations.pdf - Published Version Available under License Creative Commons Attribution. Download (2MB) | Preview

Abstract

Bayesian Networks are probabilistic graph models that can be used for classification, prediction, diagnosis and parameter learning. Probabilities can be inferred from the models and missing values can be imputed, based on probability theory. Missing person cases place a strain on the already overstretched resources of Police Forces. Such cases predominantly come from at risk groups such as children in care and people suffering from depression or dementia. Current approaches for dealing with such cases are manual and rely upon empirical studies and domain knowledge. This paper proposes the use of a Bayesian Network model, which can be used to predict the likely location of a missing person (misper) for a number of at risk groups. The model is evaluated using a set of misper cases and results compare very favourably with those of the manual processes currently used by UK Police forces. The novel approach described provides both a theoretical foundation and a practical framework for the future development of a decision support system. In addition to the model, a contribution is made through guidelines, which recount experiences in learning a Bayesian Network from data.

Item Type:	Article
Uncontrolled Keywords:	08 Information and Computing Sciences, 09 Engineering, 10 Technology
Subjects:	Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	Computer Science & Mathematics
Publisher:	Institute of Electrical and Electronics Engineers (IEEE)
Related URLs:	Author
Date Deposited:	13 Jan 2022 10:38
Last Modified:	13 Jan 2022 10:45
DOI or ID number:	10.1109/ACCESS.2021.3069081
URI:	https://researchonline.ljmu.ac.uk/id/eprint/16049

View Item