Bayesian non‐parametric spatial prior for traffic crash risk mapping: A case study of Victoria, Australia-Reference-Cited by-同舟云学术

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Bayesian non‐parametric spatial prior for traffic crash risk mapping: A case study of Victoria, Australia

Published:2022-06 Issue:2 Volume:64 Page:171-204
ISSN:1369-1473
Container-title:Australian & New Zealand Journal of Statistics
language:en
Short-container-title:Aus NZ J of Statistics

Author:

Durand J.‐B.¹,Forbes F.¹,Phan C.D.²,Truong L.²,Nguyen H.D.²³,Dama F.¹

Affiliation:

1. Univ. Grenoble Alpes Inria CNRS Grenoble INP LJK Inria Grenoble Rhone‐Alpes 655 avenue de l’Europe 38335 MontbonnotMontbonnot France

2. School of Computing, Engineering and Mathematical Sciences La Trobe University Bundoora VICAustralia

3. School of Mathematics and Physics University of Queensland St. Lucia QLDAustralia

Publisher

Wiley

Subject

Statistics, Probability and Uncertainty,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1111/anzs.12369

Reference46 articles.

1. Spatial heterogeneity of the risk of BSE in France following the ban of meat and bone meal in cattle feed

2. Analysis of Road Crash Frequency with Spatial Models

3. Finite Mixture Models for Mapping Spatially Dependent Disease Counts

4. Bayesian image restoration, with two applications in spatial statistics

5. Variational inference for Dirichlet process mixtures

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. A Festschrift for Geoff McLachlan;Australian & New Zealand Journal of Statistics;2022-06

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