STAT 578

Spring 2020

All Classes
Topics in Statistics
Course in Catalog
List of Terms offered
Register for Classes

SHOW FINAL EXAM SCHEDULE

Credit: 4 hours.

May be repeated if topics vary. Prerequisite: Consent of instructor.

My Favorites
My Schedule
Section Status updates every 10 minutes.
CRN Type Section Time Day Location Instructor Section Details
45000
Online
DSO
ARRANGED
n.a.
n.a.
Park, T
Part of Term:
1
Date Range:
01/21/20-05/06/20
Section Title:
Applied Bayesian Modeling
Section Info:
Restricted to online MCS-DS students. Additional ID Verification Coursera and ProctorU fees may apply. For more details on this course section, please see http://engineering.illinois.edu/online/courses/. Non-Degree seeking students may enroll on a space-available basis with consent. To request enrollment, please complete the “Non-Degree Enrollment Request Form” here: https://illinois.edu/fb/sec/9478165 Equivalency: CS 598 section DSO (CRN 65866) is equivalent to STAT 578 section DSO (CRN 45000). This is not true for all sections of CS 598 and STAT 578: it only applies to these specific sections in the Spring 2020 term. Since this is not an official cross-listing, they might not automatically be recognized as equivalent for your degree audit. To determine whether extra steps need to be completed for either section to count towards your degree, contact your advisor. For up-to-date information about statistics course registration, please see our registration update pages: go.illinois.edu/StatisticsRegistration TOPIC: Advanced Bayesian Modeling Description: This class meets with CS 598 section DSO (CRN 65866). Practical methods and models for Bayesian data analysis. Topics include Bayesian fundamentals, prior selection, posterior inference tools, hierarchical models, methods of Bayesian computation, model evaluation, and ordinary and generalized regression models. Emphasis on computational implementation. Prerequisites: STAT 420 and knowledge of R.
Restriction(s):
Restricted to Graduate - Urbana-Champaign.
36204
Lecture-Discussion
SDZ
10:00AM -10:50AM
MWF
205 Gregory Hall
Zhao, S
Part of Term:
1
Date Range:
01/21/20-05/06/20
Section Title:
Compound Decision Theory
Section Info:
For Statistics course registration information: go.illinois.edu/StatisticsRegistration TOPIC: Compound decision theory and empirical Bayes methods Prerequisites: Description: Compound decision problems underlie many problems in statistics, from nonparametric regression to multiple hypothesis testing. This course will introduce compound decision problems and empirical Bayes methods for solving them. Theoretical and methodological aspects of seminal developments in the field will be discussed, starting from the classical Gaussian sequence problem. Connections to modern applications will also be drawn, with an emphasis on genomics. Students will be expected to complete a research project based on the course topics.
Restriction(s):
Restricted to Graduate - Urbana-Champaign.
69104
Lecture-Discussion
XS
9:30AM -10:50AM
TR
137 Henry Administration Bldg
Shao, X
Part of Term:
1
Date Range:
01/21/20-05/06/20
Section Title:
Sufficient Dimension Reduction
Section Info:
For Statistics course registration information: go.illinois.edu/StatisticsRegistration Dimension reduction has always been a central statistical concept, and is a rapidly developing research field that has wide applications in data visualization, machine learning, signal/imaging processing, compressed sensing, functional and longitudinal data. Broadly speaking, dimension reduction techniques can be applied to essentially any large datasets with a large number of variables. In this course we will study some fundemental techniques in the 30-year old area of sufficient dimension reductions. Students will read and report on key papers in the literature and carry out a limited research project. R will be employed for computing.
Restriction(s):
Restricted to Graduate - Urbana-Champaign.
COURSE EXPLORER
Email: Course Explorer Feedback

OFFICE OF THE REGISTRAR | 901 W. Illinois Street, Urbana, Illinois 61801

Site developed by: Technology Services at Illinois | UNIVERSITY OF ILLINOIS URBANA-CHAMPAIGN
1102 Digital Computer Laboratory | MC-256 | Urbana, IL 61801 | phone 217-244-7000