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Markov random‐field models for estimating local labour markets
Author(s) -
Sebastiani Maria Rita
Publication year - 2003
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.1111/1467-9876.00398
Subject(s) - markov chain monte carlo , markov chain , econometrics , computer science , markov random field , population , bayesian inference , bayesian probability , inference , hidden markov model , random field , segmentation , machine learning , statistics , artificial intelligence , mathematics , image segmentation , demography , sociology
Summary. This work is motivated by data on daily travel‐to‐work flows observed between pairs of elemental territorial units of an Italian region. The data were collected during the 1991 population census. The aim of the analysis is to partition the region into local labour markets. We present a new method for this which is inspired by the Bayesian texture segmentation approach. We introduce a novel Markov random‐field model for the distribution of the variables that label the local labour markets for each territorial unit. Inference is performed by means of Markov chain Monte Carlo methods. The issue of model hyperparameter estimation is also addressed. We compare the results with those obtained by applying a classical method. The methodology can be applied with minor modifications to other data sets.