Investigative advising: a job for Bayes
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Post-publication record
- Nature
- Retraction
- Reason
- Duplication of Content through Error by Journal/Publisher;
- Date
- 5/19/2017 0:00
- Flagged by OpenAlex?
- Yes
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement — it reports them as false, which reads as “fine”.
Abstract
Bayesian approaches to police decision support offer an improvement upon more commonly used statistical approaches. Common approaches to case decision support often involve using frequencies from cases similar to the case under consideration to come to an isolated likelihood that a given suspect either a) committed the crime or b) has a given characteristic or set of characteristics. The Bayesian approach, in contrast, offers formally contextualized estimates and utilizes the formal logic desired by investigators. Bayes’ theorem incorporates the isolated likelihood as one element of a three-part equation, the other parts being 1) what was known generally about the variables in the case prior to the case occurring (the scientific-theoretical priors) and 2) the relevant base rate information that contextualizes the evidence obtained (the event context). These elements are precisely the domain of decision support specialists (investigative advisers), and the Bayesian paradigm is uniquely apt for combining them into contextualized estimates for decision support. By formally combining the relevant knowledge, context, and likelihood, Bayes’ theorem can improve the logic, accuracy, and relevance of decision support statements.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- Crime Science
- Topic
- Bayesian Modeling and Causal Inference
- Field
- Computer Science
- Canadian institutions
- Toronto Metropolitan University
- Funders
- Social Sciences and Humanities Research Council of Canada
- Keywords
- Prior probabilityBayes' theoremContext (archaeology)Computer scienceBayesian probabilitySuspectBayes factorMachine learningArtificial intelligencePsychologyCriminology
- Has abstract in OpenAlex
- yes