Broad Area Colloquium For AI-Geometry-Graphics-Robotics-Vision
Learning Bayesian Networks
David Heckerman
Senior Researcher
Machine Learning and Applied Statistics Group
Microsoft Research
Wednesday, January 19, 2000
refreshments 4:05PM, talk begins 4:15PM
TCseq201, Lecture Hall B
http://robotics.stanford.edu/ba-colloquium/
Abstract
For two decades, Bayesian networks--also known as directed acyclic graphical
models, belief networks, and causal networks--have been used in intelligent
systems with a fair amount of success. With few exceptions, system builders
have constructed Bayesian networks by directly encoding the knowledge of
experts. Data sets have rarely been used in the construction process. One
drawback of this knowledge-based approach is that knowledge elicitation can
be expensive. More recently, however, researchers have developed techniques
for constructing Bayesian networks (both parameters and structure) from a
combination of expert knowledge and data. These methods can significantly
reduce the cost of building an intelligent system in domains where data is
readily available. In addition, these techniques can be used to identify
causal relationships from non-experimental data--an important breakthrough
for science. I my talk, I will describe several applications of this work
being addressed at Microsoft and briefly review the technology.
About the Speaker
David Heckerman is a Senior Researcher at Microsoft Research and manager of
the Machine Learning and Applied Statistics Group. He is a co-creator of
Answer Wizard for Office 95, Office Assistant for Office 97, troubleshooters
for online support and Windows 98, and Intelligent Cross Sell for Commerce
Server. Heckerman received his Ph.D. and M.D. degrees from Stanford
University.
bac-coordinators@cs.stanford.edu
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Last modified: Mon Jan 17 19:33:36 PST 2000