The UTEA program provides eligible undergraduate students with opportunities to conduct summer research projects under the supervision of eligible U of T faculty members in the Natural Sciences & Engineering (UTEA-NSE) The UTEA is an allocation-based program, through which UTSC is allocated a limited number of UTEA awards.
The 2018 Guidlines is now posted with information regarding UTEA's and the application process are now available.
Application: 1. Both Part 1 (for students) and Part 2 (for faculty) of the application available here.
2. Student's transcript (Official or print out from Acorn is also acceptable).
Application deadline: Please email the application documents to email@example.com by Monday March 12, 2018 5PM. Applications will be adjudicated by a UTSC internal research committee if the number of applications received exceeds the allocation.
Successful applications will be notified by Friday, March 16, 2018. Only successful applications must then be uploaded by the faculty supervisor onto the UofT MRA system by Tuesday March 20, 2018.
For more information about UTEA please visit here.
2018 UTEA Project
|2018 UTEA Project(s) in CMS|
2016-2017 UTEA Projects
|2016-2017 UTEA Projects in CMS|
Prior Elicitation for Probabilities
The measurement of evidence is based on how beliefs, as measured by probabilities, change from before data is collected (a priori) to after (a posteriori). For it is data that changes beliefs concerning answers to questions of scientific interest. As such, an important, necessary part of a properly conducted statistical analysis based on a measure of evidence, requires the specification of prior beliefs. This in turn should be based on an elicitation algorithm that determines how an application expert is to choose an appropriate prior for a given problem based on their background knowledge. While this is well-recognized, work on elicitation algorithms has lagged the development of other aspects of statistical analyses. This project is concerned with the development of elicitation algorithms for the probabilities associated with categorical classifications, perhaps the most basic of all statistical models.