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INTRODUCTION TO STATISTICAL INFERENCE

STAT 610
과목 설명

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.

선수과목

Graduate/professional standing or declared in Statistics VISP

충족 요건
학점

미보고

개설 시기

미보고

평점
3.2

-9.95% 과거 데이터 대비

수료율
96.3%

-3.3% 과거 데이터 대비

A 비율
33.33%

-20.35% 과거 데이터 대비

학급 규모
27

-31.86% 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.

STATISTICAL METHODS FOR DATA SCIENCE

STAT 613

Provides a thorough grounding in modern statistical methods. Introduces statistical techniques and methods of data analysis, including data description, linear regression models, diagnostic tools, prediction and model selection, and experimental design.

STATISTICAL MODELS FOR DATA SCIENCE

STAT 611

Probability, random variables and their distributions, joint and conditional distributions, moments and inequalities, generating functions, transformations of random variables, sampling and distribution theory, convergence concepts and limit theorems for sequences of random variables.

MATHEMATICAL STATISTICS II

MATH/STAT 710

Estimation theory, efficiency, Neyman-Pearson theory of hypothesis testing, confidence regions, decision theory, likelihood ratio theory, current research topics in mathematical statistics.

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.