PE
2010
Springer
13 years 10 months ago
2010
Springer
We introduce Bayesian Expansion (BE), an approximate numerical technique for passage time distribution analysis in queueing networks. BE uses a class of Bayesian networks to appro...
IPMU
2010
Springer
13 years 10 months ago
2010
Springer
It is well known that among all probabilistic graphical Markov models the class of decomposable models is the most advantageous in the sense that the respective distributions can b...
JSAC
1998
13 years 11 months ago
1998
Abstract—We present a unified graphical model framework for describing compound codes and deriving iterative decoding algorithms. After reviewing a variety of graphical models (...
IJPRAI
1998
13 years 11 months ago
1998
We present in this paper a novel method for eliciting the conditional probability matrices needed for a Bayesian network with the help of a neural network. We demonstrate how we c...
APIN
1999
13 years 11 months ago
1999
We present a method for mapping a given Bayesian network to a Boltzmann machine architecture, in the sense that the the updating process of the resulting Boltzmann machine model pr...
JCP
2007
13 years 11 months ago
2007
Abstract— This paper deals with the use of Bayesian Networks to compute system reliability of complex systems under epistemic uncertainty. In the context of incompleteness of rel...
JAIR
2007
13 years 11 months ago
2007
The paper presents a new sampling methodology for Bayesian networks that samples only a subset of variables and applies exact inference to the rest. Cutset sampling is a network s...
TCSV
2008
13 years 11 months ago
2008
This paper describes a probabilistic framework for faithful reproduction of dynamic facial expressions on a synthetic face model with MPEG-4 facial animation parameters (FAPs) whil...
SQJ
2008
13 years 11 months ago
2008
: As business transitions into the new economy, e-system successful use has become a strategic goal. Especially in business to consumer (e-commerce) applications, users highly eval...
PRL
2006
13 years 11 months ago
2006
This paper studies two types of spatial relationships that can be learned from training examples for object recognition. The first one employs deformable relationships between obj...