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BAYESIAN STATISTICS

ECON/GENBUS/STAT 775
과목 설명

Introduces the theory, methods, and computational procedures needed to perform advanced Bayesian data analyses. Predictive and decision-theoretic motivations including subjective probability, risk, admissibility, and exchangeability; highlights key components of Bayesian analysis (i.e., prior, likelihood, posterior, and predictive distributions) within standard parametric models and advanced hierarchical and multilevel models; demonstrates the iterative process of model specification, implementation, criticism, and revision with applied case studies; implements computational techniques (e.g., Markov chain Monte Carlo, variational inference) in modern probabilistic programming languages.

선수과목
충족 요건

This course does not satisfy any prerequisites.

학점

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미보고

평점
3.79

5.78% 과거 데이터 대비

수료율
96.77%

-1.61% 과거 데이터 대비

A 비율
83.87%

28.36% 과거 데이터 대비

학급 규모
31

23.42% 과거 데이터 대비

Cumulative Grade Distribution

강사 (2026 Summr)

다음 사이트의 평점순으로 정렬 Rate My Professors

유사 과목

STATISTICAL INFERENCE FOR DATA SCIENCE

STAT 612

Statistical models, methods and theory for parameter estimation, Bayesian approach to parameter estimation, methods and theory for hypothesis tests, confidence sets, two-sample testing and ANOVA, categorical data analysis, linear regression.

BAYESIAN MACHINE LEARNING FOR MARKETING

MARKETNG 779

Learn to resolve common marketing tasks such as consumer choice prediction, market analysis, and product development by using Bayesian statistical learning methods. A broad range of methods are covered, from standard approaches such as hierarchical modeling to more advanced techniques, including Bayesian Optimization and Bayesian Deep Learning.

INTRODUCTION TO STATISTICAL INFERENCE

STAT 610

Conditioning, distribution theory, approximation to distributions, modes of convergence, limit theorems, statistical models, parameter estimation, comparision of estimators, confidence sets, theory of hypothesis tests, introduction to Bayesian inference and nonparametric estimation.

COMPUTATIONAL STATISTICS

STAT 771

Statistical inference from the perspective of computation. Statistical problems include data reduction, parameter estimation, hypothesis testing, prediction, statistical modeling, and both Bayesian and non-Bayesian inference. Computational issues include algorithms for model fitting (optimization), computing standard errors, calculating various model properties (integration), as well as systems to organize and manage data and to deploy computations.

APPLIED BAYESIAN STATISTICS FOR EDUCATION RESEARCH

EDPSYCH 965

Covers the basic elements of Bayesian statistics through lecture, discussion and practice. Focuses on why the Bayesian perspective provides a powerful alternative to the frequentist perspective. Topics to be covered include; Bayes' theorem, Markov chain Monte Carlo sampling and the "rjags" program, Bayesian hypothesis testing, Bayesian model building and evaluation, Bayesian approaches to missing data, Bayesian model averaging, Bayesian multilevel models and latent variable models, and philosophical debates within the Bayesian paradigm of statistics.