Difference between revisions of "stat341 / CM 361"

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Until recently, the use of probabilistic and statistical models has been limited by the complexity of exact inference.  However, recent advances from statistics computing science have made many inference tasks practical.  This course will provide the fundamentals and algorithms for computational statistics.  Examples will be given of applications of these models in different areas.
 
Until recently, the use of probabilistic and statistical models has been limited by the complexity of exact inference.  However, recent advances from statistics computing science have made many inference tasks practical.  This course will provide the fundamentals and algorithms for computational statistics.  Examples will be given of applications of these models in different areas.
  
==Topics==
+
==Lecture Content==
===[[Monte Carlo methods]]===
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===Random Sampling and Inverse Transform Method===
 
 
===[[Computational inference]]===
 
 
 
===[[Data partitioning and resampling]]===
 
 
 
===[[Numerical methods in statistics]]===
 
 
 
===[[Nonparametric probability density estimation]]===
 
 
 
===[[Stastical models and data fitting]]===
 

Revision as of 10:51, 12 May 2009

Computational Statistics and Data Analysis is a course offered at the University of Waterloo, currently being taught by Ali Ghodsi.

Description

Until recently, the use of probabilistic and statistical models has been limited by the complexity of exact inference. However, recent advances from statistics computing science have made many inference tasks practical. This course will provide the fundamentals and algorithms for computational statistics. Examples will be given of applications of these models in different areas.

Lecture Content

Random Sampling and Inverse Transform Method