Announcements
- Homework 7 is now posted.
What will the course be about?
- The statistics
theme of STAT 535 is graphical probability models (aka belief
networks), an outstanding example of statistics and algorithms at
work together. You will learn the basics of belief networks in
general, focussing on tree belief nets for the purpose of
implementation.
- You will learn and implement some clever
general purspose algorithms.
- You will learn about object
oriented programming and related software engineering concepts.
-
You will get hands-on experience with developing a project in Java.
Who is this class for?
This class is intended for statistics and biostatistics students who have taken STAT 534, or for other graduate students with an interest in statistics, algorithms and computing.
Prerequisites
Either STAT 534 OR
- A course in probability, including basic notions of multivariate analysis (conditional probability, marginals).
- Algorithms and data structure at a basic level (arrays, lists, sets, O( ) notation).
- Knowledge of a computer programming language (like C, C++, Java but not Matlab, Splus)
Instructor: Marina Meila
mmp@stat.washington.edu
Lectures: Tuesdays & Thursdays 11:30 - 12:50 in EE1 003
Office hours (may change!): Tuesdays 2:30 -- 3:30 in Padelford B - 321
Course home page: http://www.ms.washington.edu/stat535/fall06 (this page)
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