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CDA 531

Probability and Data Analysis

University at Buffalo 3 credits Computational Data-Enabled Sci

Course Description

Topics include: review of probability, conditional probability, Bayes’ Theorem; random variables and distributions; expectation and properties; covariance, correlation, and conditional expectation; special distributions; Central Limit Theorem and applications; estimations, including Bayes; estimators, maximum likelihood estimators, and their properties. Includes use of sufficient statistics to ‘improve’ estimators, distribution of estimators, unbiasedness, hypothesis testing, linear statistical models, and statistical inference from the Bayesian point of view.

Code
CDA 531
Credits
3
Subject
Computational Data-Enabled Sci

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