Abstract
Inference for spatial data arises, for example in medical imaging, epidemiology, ecology, and other areas, and gives rise to specific challenges for nonparametric Bayesian modeling. In this chapter we briefly review the fast growing related literature and discuss two specific models in more detail. The two models are the CAR SSM (species sampling with conditional autoregression) prior of Jo et al. (Dependent species sampling models for spatial density estimation. Technical report, Department of Statistics, Seoul National University, 2015) and the spatial PPM (product partition model) of Page and Quintana (Spatial product partition models. Technical report, Pontificia Universidad Católica de Chile, 2015).
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Acknowledgements
Peter Müller’s research was partially supported by NIH R01 CA132897. Fernando A. Quintana’s research was partially funded by grant FONDECYT 1141057. Garritt L. Page’s research was partially funded by grant FONDECYT 11121131. Jaeyong Lee was partially supported by Advanced Research Center Program (S/ERC), a National Research Foundation of Korea grant funded by the Korea government (MSIP) (2011-0030811). Seongil Jo’s research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2014R1A6A3A01059555).
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Jo, S., Lee, J., Page, G., Quintana, F., Trippa, L., Müller, P. (2015). Spatial Species Sampling and Product Partition Models. In: Mitra, R., Müller, P. (eds) Nonparametric Bayesian Inference in Biostatistics. Frontiers in Probability and the Statistical Sciences. Springer, Cham. https://doi.org/10.1007/978-3-319-19518-6_18
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DOI: https://doi.org/10.1007/978-3-319-19518-6_18
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