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The Bayesian approach seeks to incorporate every piece of relevant information when making decisions. It combines prior insights from expertise, expert opinions, or historical data with present observations to define the current understanding. In today's age of massive, imperfect datasets (with gaps, censored entries, etc.) and the need for sophisticated statistical models, computational Bayesian techniques have gained significant traction. These approaches enable the implementation of intricate physical models that were once computationally prohibitive. The theoretical foundations of Bayesian methods provide deeper insights into challenges common across numerous real-world applications. Our researchers create Bayesian techniques for diverse fields such as environmental studies, medical technology, aerospace engineering, and defense systems.
An online application form. This includes contact information, previous degrees, etc.
A transcript from each postsecondary institution. One unofficial transcript from each institution should be submitted online. If accepted and enrolled, official transcripts will be required later.
Three letters of recommendation. The applicant provides email addresses for letter writers to the application system. The letters should be submitted online directly by the letter writers.
A written personal statement describing your academic and career goals, as well as special interests in the area of statistics. The statement should not exceed two pages and should be uploaded online.
A resume or curriculum vita is recommended. It must be uploaded online.
GRE scores from the general and subject tests are not required for admission to the Statistics Graduate Programs including both master and PhD programs.