Abstract
Using a probabilistic model, exact and approximate probability density functions (PDFs) for city-block distances and distance ratios are developed. The model assumes that stimuli can be represented by random vectors having multivariate normal distributions. Comparisons with the more common Euclidean PDFs are presented. The potential ability of the proposed model to correctly detect Euclidean and city-block metrics is briefly investigated. These results are then contrasted to those obtained using a deterministic, nonmetric model.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
