APPLIED BAYESIAN STATISTICS FOR EDUCATION RESEARCH

Name: APPLIED BAYESIAN STATISTICS FOR EDUCATION RESEARCH
Availability: OutOfStock
Rating: 4.8 (60 reviews)

EDPSYCH 965

과목 설명

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.

선수과목

EDPSYCH 763

충족 요건

This course does not satisfy any prerequisites.

학점

미보고

개설 시기

미보고

평점

3.67% 과거 데이터 대비

수료율

100%

3.45% 과거 데이터 대비

A 비율

100%

5.26% 과거 데이터 대비

학급 규모

-20% 과거 데이터 대비

Cumulative Grade Distribution

강사 (2026 Summr)

다음 사이트의 평점순으로 정렬 Rate My Professors

유사 과목

STATISTICAL INFERENCE FOR DATA SCIENCE

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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.

ADVANCED SEMINAR IN EDUCATIONAL MEASUREMENT AND STATISTICS

EDPSYCH 971

Discuss a different current article from the educational statistics and measurement literature. Explore a variety of subjects in which one might like to eventually conduct research.

BAYESIAN STATISTICS

ECON/GENBUS/STAT 775

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.

FUNDAMENTAL STATISTICS IN LEARNING ANALYTICS

EDPSYCH 505

Logic and methods of statistics used in the field of learning analytics. Emphasizes concepts and application rather than computational details to build fundamentals in the areas of inferential statistics. Reliability and validity, scalability, and the implications of inference in applied statistical methodology.

STATISTICAL METHODS APPLIED TO EDUCATION I

EDPSYCH 760

Introductory descriptive statistics and statistical inference; measures of central tendency and variability, confidence intervals, theory of hypothesis testing, correlation techniques.