STAT 578

Spring 2021

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Topics in Statistics
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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
45000
Online
DSO
ARRANGED
n.a.
n.a.
Park, T
Part of Term:
1
Date Range:
01/25/21-05/05/21
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 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.
69104
Online
JL
ARRANGED
n.a.
n.a.
Liu, J
Part of Term:
1
Date Range:
01/25/21-05/05/21
Section Title:
High-Dimensional Statistics
Section Info:
Nonparametric/high-dimensional statistical models usually concern the case where the number of features/parameters is large compared to the number of independent samples. Examples include Lasso, graphical Lasso, matrix estimation, principal component analysis. A few techniques for analysis of these models may be introduced, depending on the availability of time and interests: information-theoretic (minimax) lower bounds, restricted eigenvalue conditions, nullspace conditions, statistical dimension, matrix concentration, statistical physics predication (replica analysis), iterative algorithms, approximate message passing. Prerequisites: STAT 432 For Statistics course registration information: go.illinois.edu/StatisticsRegistration
Restriction(s):
Restricted to Graduate - Urbana-Champaign.
72931
Online
SAC
11:00AM -12:20PM
MW
n.a.
Culpepper, S
Part of Term:
1
Date Range:
01/25/21-05/05/21
Section Title:
Adv Latent Variable Modeling
Section Info:
We consider the use of latent variables for multivariate statistical models for continuous and discrete data as found in educational, psychological, social, behavioral, and health research. We explore Bayesian computational approaches for inferring model parameters and review recent work in areas such as exploratory factor analysis and mixture modeling. We also discuss issues related to statistical computing via R and Rcpp. Prerequisites: STAT 410 or equivalent, Knowledge of R programming For Statistics course registration information: go.illinois.edu/StatisticsRegistration
Restriction(s):
Restricted to Graduate - Urbana-Champaign.
36204
Online
TP
ARRANGED
n.a.
n.a.
Park, T
Part of Term:
1
Date Range:
01/25/21-05/05/21
Section Title:
Bayesian Analysis&Computation
Section Info:
An MS-level introduction to the basic theory and modern practice of Bayesian analysis. Topics include elements of Bayesian theory and inference, hierarchical modeling, model checking and comparison, Markov chain Monte Carlo (Gibbs, Metropolis, Hamiltonian), Bayesian regression, and additional topics as time permits. Implementations in R and MCMC packages. Prerequisites: STAT 425. For Statistics course registration information: go.illinois.edu/StatisticsRegistration
Restriction(s):
Restricted to Graduate - Urbana-Champaign.
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