A Bayesian Approach to Disease Clustering
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In the disease clustering problem, only a few Bayesian approach have been applied. Gangnon and Clayton [1] is one of those few and they developed an approach to make an inference on the parameters of a simple model (Poisson-Gamma) for spatial clustering. Their method do not require cluster locations, shapes, or boundaries to be pre-specified. They rather assume a prior information of the clusters (such as shapes, sizes, and etc.) using a class of priors on a Markov connected component field (MCCF). In this work, we first implemented their approach using the free statistical software R and also proposed a couple of ways to improve the efficiency of the algorithm and reduce the computing time. Based on the results, we will propose a new tree-based method, which can improve the algorithm further more, in the future.
Abstract
In the disease clustering problem, only a few Bayesian approach have been applied. Gangnon and Clayton [1] is one of those few and they developed an approach to make an inference on the parameters of a simple model (Poisson-Gamma) for spatial clustering. Their method do not require cluster locations, shapes, or boundaries to be pre-specified. They rather assume a prior information of the clusters (such as shapes, sizes, and etc.) using a class of priors on a Markov connected component field (MCCF). In this work, we first implemented their approach using the free statistical software R and also proposed a couple of ways to improve the efficiency of the algorithm and reduce the computing time. Based on the results, we will propose a new tree-based method, which can improve the algorithm further more, in the future.
참고문헌 (19)
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