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INTRODUCTION TO COMPUTATIONAL STATISTICS

COMPSCI/STAT 471
과목 설명

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).

선수과목

MATH/STAT 310 and (STAT 333 or STAT 340 ), graduate/professional standing, or declared in Statistics VISP

충족 요건

This course does not satisfy any prerequisites.

학점

미보고

개설 시기

미보고

평점
2.5

-26.1% 과거 데이터 대비

수료율
78.95%

-18.85% 과거 데이터 대비

A 비율
26.32%

-40.95% 과거 데이터 대비

학급 규모
19

-38.04% 과거 데이터 대비

Cumulative Grade Distribution

강사 (2026 Summr)

다음 사이트의 평점순으로 정렬 Rate My Professors

유사 과목

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 METHODS II

STAT 602

Provides a thorough grounding in modern statistical methods. The specific learning outcomes for the course are to understand data collection in context (how/why data were collected, key questions under study); explore data by effective graphical and numerical summaries; understand probability concepts and models as tools for studying random phenomena and for statistical inference; analyze data using appropriate, modern statistical models, methods, and software; understand the statistical concepts underlying methods; develop the ability to interpret results and critically evaluate the methods used; communicate data analysis and key findings in context.

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.

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.

STATISTICAL METHODS I

STAT 601

Provides a thorough grounding in modern statistical methods. The specific learning outcomes for the course are to understand data collection in context (how/why data were collected, key questions under study); explore data by effective graphical and numerical summaries; understand probability concepts and models as tools for studying random phenomena and for statistical inference; analyze data using appropriate, modern statistical models, methods, and software; understand the statistical concepts underlying methods; develop the ability to interpret results and critically evaluate the methods used; communicate data analysis and key findings in context.