The Bayesian t-test and beyond

Methods in Molecular Biology
Mithat Gönen

Abstract

In this chapter we will explore Bayesian alternatives to the t-test. We saw in Chapter 1 how t-test can be used to test whether the expected outcomes of the two groups are equal or not. In Chapter 3 we saw how to make inferences from a Bayesian perspective in principle. In this chapter we will put these together to develop a Bayesian procedure for a t-test. This procedure depends on the data only through the t-statistic. It requires prior inputs and we will discuss how to assign them. We will use an example from a microarray study as to demonstrate the practical issues. The microarray study is an important application for the Bayesian t-test as it naturally brings up the question of simultaneous t-tests. It turns out that the Bayesian procedure can easily be extended to carry several t-tests on the same data set, provided some attention is paid to the concept of the correlation between tests.

Citations

Jan 1, 2014·Journal of Bioinformatics and Computational Biology·Henry HanZhou Ji
Jan 13, 2015·Cancer Informatics·Henry Han, Xiaoqian Jiang
Apr 14, 2018·PloS One·Harlan Campbell, Paul Gustafson

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