STAT 578

Fall 2019

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Credit: 4 hours.

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

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CRN Type Section Time Day Location Instructor Section Details
48733
Online
DSO
ARRANGED
n.a.
n.a.
Park, T
Part of Term:
1
Date Range:
08/26/19-12/11/19
Section Title:
Advanced 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 Sections (and CRNs) for on-campus, degree-seeking students are: STAT 578 A1 (30959). Equivalency: CS 598 section DSO (CRN 69343) is equivalent to STAT 578 section DSO (CRN 48733). This is not true for all sections of CS 598 and STAT 578: it only applies to these specific sections in the Fall 2019 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 69343). 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. Restricted to MCS:Computer Sci Online -UIUC or NDEG:Computer Science Onl-UIUC.
71915
Lecture-Discussion
GF
12:30PM -1:50PM
TR
136 Loomis Laboratory
Fellouris, G
Part of Term:
1
Date Range:
08/26/19-12/11/19
Section Title:
Sequential Detection
Section Info:
The focus of this course will be on testing hypotheses and detecting changes when the data are collected in real time and the goal is to make an accurate decision as quickly as possible. Problems of design (controlled sensing) will also be considered, where one needs to specify a sampling mechanism in addition to a stopping rule and a decision rule.
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