Application of Hierarchical Bayesian Model in Ophtalmological Study
Date Issued
2019
Author(s)
Ljubic, Antonela
Abstract
The problems with statistical results based on p-values, together with multiple comparisons have been criticized often in the literature. Many authors argue that this way of reporting scientific research creates unreliable results. This issue is especially important in the era of Big Data, when many tests are done on the same data sets, which are often openly available. A way to overcome these problems is offered by Bayesian analysis. In our previous research we have used traditional
statistical approach to conduct multiple hypothesis tests on our data in ophtalmological study. The goal of this paper is to apply the hierarchical Poisson exponential model on the data and test the dependence of congenital heart disease and Brusfield spots. We give detailed description
of the model, analyze the generated Markov chains and the posterior distributions for the simulated parameters and discuss the results from Bayesian perspective. The results are original and have not been published yet.
statistical approach to conduct multiple hypothesis tests on our data in ophtalmological study. The goal of this paper is to apply the hierarchical Poisson exponential model on the data and test the dependence of congenital heart disease and Brusfield spots. We give detailed description
of the model, analyze the generated Markov chains and the posterior distributions for the simulated parameters and discuss the results from Bayesian perspective. The results are original and have not been published yet.
Subjects