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COMPUTATIONAL STATISTICS

STAT 771
과목 설명

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.

선수과목

Declared in Statistics MS, Statistics PhD, Biomedical Data Science PhD, Biomedical Data Science MS, Business PhD, or Statistics Doctoral Minor

충족 요건

This course does not satisfy any prerequisites.

학점

미보고

개설 시기

미보고

평점
3.7

2.1% 과거 데이터 대비

수료율
100%

과거 데이터 대비 변화 없음

A 비율
60.87%

9.25% 과거 데이터 대비

학급 규모
23

-1.43% 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.

INTRODUCTION TO COMPUTATIONAL STATISTICS

COMPSCI/STAT 471

Classical statistical procedures arise where closed-form mathematical expressions are available for various inference summaries (e.g. linear regression; analysis of variance). A major emphasis of modern statistics is the development of inference principles in cases where both more complex data structures are involved and where more elaborate computations are required. Topics from numerical linear algebra, optimization, Monte Carlo (including Markov chain Monte Carlo), and graph theory are developed, especially as they relate to statistical inference (e.g., bootstrapping, permutation, Bayesian inference, EM algorithm, multivariate analysis).

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.

DATA SCIENCE MODELING I

STAT 240

Introduces reproducible data management, modeling, analysis, and statistical inference through a practical, hands-on case studies approach. Topics include the use of an integrated statistical computing environment, data wrangling, the R programming language, data graphics and visualization, random variables and concepts of probability including the binomial and normal distributions, data modeling, statistical inference in one- and two- sample settings for proportions and means, simple linear regression, and report generation using R Markdown with applications to a wide variety of data to address open-ended questions.

INTRODUCTION TO PROBABILITY AND MATHEMATICAL STATISTICS II

MATH/STAT 310

Mathematical statistical inference aims at providing an understanding of likelihood's central role to statistical inference, using the language of mathematical statistics to analyze statistical procedures, and using the computer as a tool for understanding statistics. Specific topics include: samples and populations, estimation, hypothesis testing, and theoretical properties of statistical inference.