Department of Computer Science
University of Texas at San Antonio
Schedule:
References:
References:
[1] Zaijun Hu; Vollmar, G., "Towards XML metamodel patterns for XML data
modeling" 12th International Workshop on Database and Expert Systems
Applications, Sept. 2001.
Abstract:
Modelling sequential data is important in many areas of science and
engineering. Hidden Markov models
(HMMs) and Kalman filter models (KFMs) are popular for this because they are
simple and flexible. For
example, HMMs have been used for speech recognition and bio-sequence
analysis, and KFMs have been
used for problems ranging from tracking planes and missiles to predicting
the economy. However, HMMs
and KFMs are limited in their "expressive power". Dynamic Bayesian Networks
(DBNs) generalize HMMs
by allowing the state space to be represented in factored form, instead of
as a single discrete random variable.
DBNs generalize KFMs by allowing arbitrary probability distributions, not
just (unimodal) linear-Gaussian.
In this talk, I will lead you to review Dr. Kevin Murphy's thesis and
discuss how to represent many different kinds of models as DBNs, how to
perform exact and approximate inference in DBNs, and how to learn DBN models
from sequential data.
Abstract:
Modelling sequential data is important in many areas of science and
engineering. Hidden Markov models
(HMMs) and Kalman filter models (KFMs) are popular for this because they are
simple and flexible. For
example, HMMs have been used for speech recognition and bio-sequence
analysis, and KFMs have been
used for problems ranging from tracking planes and missiles to predicting
the economy. However, HMMs
and KFMs are limited in their "expressive power". Dynamic Bayesian Networks
(DBNs) generalize HMMs
by allowing the state space to be represented in factored form, instead of
as a single discrete random variable.
DBNs generalize KFMs by allowing arbitrary probability distributions, not
just (unimodal) linear-Gaussian.
In this talk, I will lead you to review Dr. Kevin Murphy's thesis and
discuss how to represent many different kinds of models as DBNs, how to
perform exact and approximate inference in DBNs, and how to learn DBN models
from sequential data.
Please send emails to carola@cs.utsa.edu, or seminar co-organizers: Kay Robbins, Weining Zhang, Yufei Huang, Carola Wenk, and Qi Tian.