The Bayesian Statistics Training (BST) provides workshops and hands-on data analytics for those who are interested in Bayesian approach to statistical inference. The workshop introduces the fundamental of Bayesian statistics and modeling in the context of epigenomics and public health, such as choice of prior distribution and posterior computation. The examples and projects are done using R programming.
The BST aims to support students in learning emerging skillset in the field of Bayesian statistics and data science through workshops and undergraduate research. The BST also connects students with resources to advance their skills in statistics and data science. The students will have opportunities to explore and navigate career paths and learn more about the Statistics and Data Science graduate programs at Western Michigan University.
Dr. Duy Ngo.
Assistant Professor of Statistics.
Han Kha.
Ph.D. Student in
Statistics.
Geraldine Laydia.
Ph.D. Student
in Statistics.
Senuri Rose Niseka Gunaratne.
Ph.D. Student in Statistics.
Michael Loh.
Undergraduate
Student.
Hannah Athirah.
Undergraduate Student.
Daniel Quartey, Ph.D.
Medtronic.
Xi Qiao, Ph.D.
Case Western
Reserve University.
The Bayesian Statistics Training is sponsored by the U.S. National Science Foundation.