Tue 22nd July 2014
Jinyuan Zhang wins Best Poster Award at ARC
MSc. candidate Jinyuan Zhang won the Best Poster Award at the Actuarial Research Conference held at the UC Santa Barbara, CA on July 13-16, 2014. Jinyuan's poster is titled “Conditional Extremes in Asymmetric Financial Markets”, and is based on part of her MSc. project. Jinyuan is supervised by Professor Natalia Nolde.
Fri 4th July 2014
STAT 357/EECE 357 Stochastic Signals and Systems
The Statistics Department will be offering this new course, in cooperation with the Electrical and Computer Engineering Department. Although the course was developed for undergraduate students in Electrical Engineering, the material should be of interest to students in Statistics. In particular, students interested in data science methods in signal processing will find this course a good introduction.
Either EECE 357 or STAT 357 will count towards your statistics program requirements.
For book-keeping purposes, the course is offered as EECE in term 1 and STAT in term 2. However, the course is the same in both terms, and you can register in either term.
EECE 357 has 4 credits and includes 2 hours of Lab/Tutorial every week.
Prerequisite: EECE 269
Note: EECE 269 is offered in the fall, and is open to Statistics majors. The prerequisite for EECE 269 is MATH 256 or MATH 267, or MATH 255 AND MATH 257.
The major topics in the course include some material you may have already encountered: in particular, in probability. There will be new material and an emphasis on applications to signal processing. Topics will include discrete and continuous random vectors, random processes, and modeling and estimation of linear time-invariant systems.
The first half of the course, taught by a Statistics professor, gives an introduction to probability and statistics including discrete and continuous random vectors, variables transformations, least squares, maximum likelihood, law of large numbers and central limit theorem. The second half of the course, taught by an EECE professor, will provide students with a solid foundation in stochastic signals and systems, with an emphasis on engineering applications in domains such as communication, signal processing and control systems.