- All Categories
- Featured Events
- Alumni
- Application Deadline
- Arts
- Campus Discourse
- Careers
- BU Central
- Center for the Humanities
- Charity & Volunteering
- Kilachand Center
- Commencement
- Conferences & Workshops
- Diversity & Inclusion
- Examinations
- Food & Beverage
- Global
- Health & Wellbeing
- Keyword Initiative
- Lectures
- LAW Community
- LGBTQIA+
- Meetings
- Orientation
- Other Events
- Religious Services & Activities
- Special Interest to Women
- Sports & Recreation
- Social Events
- Study Abroad
- Weeks of Welcome
- Re-Birth - Art Installation by Sheila Pree Bright12:00 am
- Hostile Terrain 94 - InstallationAll day
- "Who Is My Neighbor?" Art by John August Swanson6:00 am
- CCD Event: Networking for International Students8:00 am
- SHS Immunization Clinic9:00 am
- Employer Info Session: Urban Teachers12:00 pm
- Reddit I/AmA with Dr. Nick Wagner12:00 pm
- Questrom School of Business Info Session1:00 pm
- Classroom + Career Q&A: Mid-Semester & Midterm Study Help2:30 pm
- Kids' Night; presented by the BU Tri Delts4:00 pm
- On Bayesian sparse canonical correlation analysis via Rayleigh quotient framework (Qiuyun Zhu -- Boston University)4:00 pm
- Study Smarter, Not Harder: Effective Note Taking & Study Strategies4:00 pm
- CCD Workshop: Interviewing in a Virtual World4:00 pm
- CCD Workshop: LinkedIn 1015:00 pm
- Faculty Talk: Screenprinting with Melanin Produced by E. coli5:00 pm
- Employer Info Session: Converse5:00 pm
- Classroom + Career Q&A: Mid-Semester & Midterm Study Help5:30 pm
- Mind, Body, Spirit Yoga5:30 pm
- Give Me the Listening Ear: What Does Being a Black Coach Mean?6:00 pm
- The Launch of AGNI 92: Poetry from the fall issue & AGNI Online6:30 pm
- Amirah Sacket (Hip Hop Dancer) Lecture and Demonstration8:30 pm
On Bayesian sparse canonical correlation analysis via Rayleigh quotient framework (Qiuyun Zhu -- Boston University)
We propose a semi-parametric Bayesian method for the principal canonical pair that employs the scaled Rayleigh quotient as a quasi-log-likelihood with the spike-and-slab prior as the sparse constraints. Our approach does not require a complete joint distribution of the data, and as such, is more robust to non-normality than current Bayesian methods. Moreover, simulated tempering is used for solving the multi-modality problem in the resulting posterior distribution. We study the numerical behavior of the proposed method on both continuous and truncated data, and show that it compares favorably with other methods. As an application, we use the methodology to maximally correlate clinical variables and proteomic data for a better understanding of covid-19 disease. Our analysis identifies the protein Alpha-1-acid glycoprotein 1 (AGP 1) as playing an important role in the progression of Covid-19 into a severe illness.
When | 4:00 pm to 5:00 pm on Thursday, October 22, 2020 |
---|---|
Location | Online (Zoom) - Email Mickey Salins (msalins@bu.edu) for more information |