Validation of a bayesian belief network representation for posterior probability calculations on national crime victimization survey
David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Artificial Intelligence and Law 16 (3):245-276 (2008)
This paper presents an effort to induce a Bayesian belief network (BBN) from crime data, namely the national crime victimization survey (NCVS). This BBN defines a joint probability distribution over a set of variables that were employed to record a set of crime incidents, with particular focus on characteristics of the victim. The goals are to generate a BBN to capture how characteristics of crime incidents are related to one another, and to make this information available to domain specialists. The novelty associated with the study reported in this paper lies in the use of a Bayesian network to represent a complex data set to non-experts in a way that facilitates automated analysis. Validation of the BBN’s ability to approximate the joint probability distribution over the set of variables entailed in the NCVS data set is accomplished through a variety of sources including mathematical techniques and human experts for appropriate triangulation. Validation results indicate that the BBN induced from the NCVS data set is a good joint probability model for the set of attributes in the domain, and accordingly can serve as an effective query tool.
|Keywords||National crime victimization survey Bayesian belief network Machine learning Probabilistic query Posterior probability calculations Joint probability distribution Model validation|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
No references found.
Citations of this work BETA
No citations found.
Similar books and articles
Richard Bradley (2007). A Unified Bayesian Decision Theory. Theory and Decision 63 (3):233-263,.
Kamal Dahbur & Thomas Muscarello (2003). Classification System for Serial Criminal Patterns. Artificial Intelligence and Law 11 (4):251-269.
Arthur I. Fine (1968). Logic, Probability, and Quantum Theory. Philosophy of Science 35 (2):101-111.
Jonas Clausen Mork (2013). Uncertainty, Credal Sets and Second Order Probability. Synthese 190 (3):353-378.
Cory F. Juhl (1996). Objectively Reliable Subjective Probabilities. Synthese 109 (3):293 - 309.
Timothy Herron, Teddy Seidenfeld & Larry Wasserman (1994). The Extent of Dilation of Sets of Probabilities and the Asymptotics of Robust Bayesian Inference. PSA: Proceedings of the Biennial Meeting of the Philosophy of Science Association 1994:250 - 259.
Patrick Maher (2010). Bayesian Probability. Synthese 172 (1):119 - 127.
Added to index2009-01-28
Total downloads11 ( #194,000 of 1,696,464 )
Recent downloads (6 months)5 ( #113,165 of 1,696,464 )
How can I increase my downloads?