Announcements
- Some typos were fixed in Lecture 5 (Variable Elimination) - 10/16/07
What will the course be about?
- The theme of
STAT 535 is graphical probability models (aka belief
networks), an outstanding example of statistics and algorithms at
work together. Emphasis will be on applying graphical models to data
analysis, and on the algorithmic, computational and practical aspects
of these models.
- In addition, you will occasionally explore other topics in the
probability and statistics of discrete structures and
you will learn and implement some clever
general purspose algorithms.
Who is this class for?
This class is intended for
statistics and biostatistics students who have taken STAT 534, but is
regularly attended by 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,
Matlab, R, Splus)
Instructors: Marina Meila
mmp at stat dot washington dot edu
Carl de Marcken
cgdemarc at stat dot washington dot edu
Lectures: Tuesdays & Thursdays 11:30 - 12:50 in EEB 042
Office hours: Marina Meila, Monday 2-3 pm in PDL B-321
Carl de Marcken, Thursday 1-2pm in CSE 346
Course home page: http://www.stat.washington.edu/courses/stat535/fall07 (this page)
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