Colleagues,
just to remind you that Nir Friedman and I will be giving a tutorial on
Learning Bayesian Networks from Data this year at IJCAI. This tutorial
will be a revised version of the one we taught last year at AAAI-99.
Slides for the tutorial given at AAAI-99 can be found at
www.cs.huji.ca.il/~nir. I am including a description (txt) with this
message.
Apologies if you receive this msg more than once....
Cheers,
Moises
Bayesian networks are compact and computationally efficient
representations of probability distributions. Over the last decade,
they have become the method of choice for the representation of
uncertainty in artificial intelligence. Today, they play a crucial
role in modern expert systems, diagnosis engines, and decision support
systems.
In recent years, there has been significant progress in methods and
algorithms for inducing Bayesian networks directly from data.
Learning these particular models is desirable for several
reasons. First, there is a wide array of off-the-shelf tools that can
apply the learned models for prediction, decision making and
diagnosis. Second, learning Bayesian networks also provide a
principled approach for semi-parametric density estimation, data
analysis, pattern classification, and modeling. Third, in some
situations they allow us to provide causal interpretation of the
observed data. Fourth, they allow to combine knowledge engineered
models with information from raw data.
In this tutorial we will first review the basic concepts behind
Bayesian networks. We will then describe the fundamental theory and
algorithms for inducing these networks from data including learning
the parameters and the structure of the network, how to handle missing
values and hidden variables, and how to learn causal models. Finally,
we will discuss advanced methods, open research areas, and relate to
applications such as pattern matching and classification, speech
recognition, data analysis, and scientific discovery.
This tutorial is intended for people interested in data analysis,
datamining, pattern classification, machine learning and reasoning
under uncertainty. Familiarity with the basic concepts of probability
theory will be helpful.
