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      Spatio-temporal modelling of crime using low discrepancy sequences

      Brown, Paul T.; Joshi, Chaitanya; Joe, Stephen; McCarter, Nigel
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      IWSM2016-Brown etal.pdf
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       dupuy.perso.math.cnrs.fr
      Citation
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      Brown, P. T., Joshi, C., Joe, S., & McCarter, N. (2016). Spatio-temporal modelling of crime using low discrepancy sequences. In Proceedings of the 31st International Workshop on Statistical Modelling Volume II, 4–8 July 2016, Rennes, France.
      Permanent Research Commons link: https://hdl.handle.net/10289/10770
      Abstract
      We perform spatio-temporal modelling of burglary data in order to predict areas of high criminal risk for local authorities. We wish to account for several spatio-temporal factors as latent processes to make the model as realistic as possible, thus creating a model with a large latent eld with several hyperparameters. Analysis of the model is done using Integrated Nested Laplace Approximations (INLA) (Rue et al. 2009), a fast Bayesian inference methodology that provides more computationally efficient estimations than Markov Chain Monte Carlo (MCMC) methods.
      Date
      2016
      Type
      Conference Contribution
      Rights
      This paper was published as a part of the proceedings of the 31st International Workshop on Statistical Modelling, INSA Rennes, 4–8 July 2016. The copyright remains with the author(s).
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      • Computing and Mathematical Sciences Papers [1431]
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