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INTRODUCTION TO COMPUTATIONAL STATISTICS

COMPSCI/STAT 471
Course Description

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).

Prerequisties

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

Satisfies

This course does not satisfy any prerequisites.

Credits

3

Offered

Occasionally

Grade Point Average
2.5

-26.1% from Historical

Completion Rate
78.95%

-18.85% from Historical

A Rate
26.32%

-40.95% from Historical

Class Size
19

-38.04% from Historical

Instructors (2025 Fall)

Sorted by ratings from Rate My Professors

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