Probabilistic multidimensional scaling using a city-block metric
type: Item
instance of: scholarly article
title: Probabilistic multidimensional scaling using a city-block metric
zbMATH Open document ID: 1011.91076
author: David B. MacKay
published in: Journal of Mathematical Psychology
publication date: 2003-05-31T00:00:00Z
review text: Exact and approximate, computationally efficient Probability Density Functions (PDFs) for city-block distances and distance ratios are considered. The main assumption of the presented probabilistic model is that stimuli can be represented by random vectors having multivariate normal distributions. As the stimulus variance magnitudes increase, the differences between city-block and Euclidean PDFs diminish. The problem of distinguishing a city-block or Euclidean metric under different levels of stimulus variability by models based upon city-block PDFs is briefly discussed.
reviewed by: Anna Gomolińska
Mathematics Subject Classification ID: 91C15
Mathematics Subject Classification ID: 91E30
zbMATH DE Number: 1750020
zbMATH Keywords: probabilistic city-block metric
zbMATH Keywords: probabilistic Euclidean metric
zbMATH Keywords: probability density functions
zbMATH Keywords: multidimensional scaling
describes a project that uses: ProScaL
describes a project that uses: KYST
MaRDI profile type: Publication
OpenAlex ID: W2015129310
Wikidata QID: Q52065864
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DOI: 10.1006/JMPS.2000.1311
description: scientific article; zbMATH DE number 1750020