A Bayesian hierarchical point process model for epidermal nerve fiber patterns
Artikel i vetenskaplig tidskrift, 2019

We introduce the Thomas process in a Bayesian hierarchical setting as a model for point pattern data with a nested structure. This model is applied to a nerve fiber data set which consists of several point patterns of nerve entry points from 47 subjects divided into 3 groups, where the grouping is based on the diagnosed severity of a certain nerve disorder. The modeling assumption is that each point pattern is a realization of a Thomas process, with parameter values specific to the subject. These parameter values are in turn assumed to come from distributions that depend on which group the subject belongs to. To fit the model, we construct an MCMC algorithm, which is evaluated in a simulation study. The results of the simulation study indicate that the group level mean of each parameter is well estimated, but that the estimation of the between subject variance is more challenging. When fitting the model to the nerve fiber data, we find that the structure within clusters appears to be the same in all groups, but that the number of clusters decreases with the progression of the nerve disorder.

Hierarchical structure

Bayesian estimation

Epidermal nerve fibers

Nerve entry points

Thomas process

Cluster point process


Claes Andersson

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Tuomas Rajala

University College London (UCL)

Aila Särkkä

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Mathematical Biosciences

0025-5564 (ISSN)

Vol. 313 48-60


Annan medicinteknik

Bioinformatik (beräkningsbiologi)

Sannolikhetsteori och statistik





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