Sciweavers

UAI
2001
14 years 25 days ago
Improved learning of Bayesian networks
The search space of Bayesian Network structures is usually defined as Acyclic Directed Graphs (DAGs) and the search is done by local transformations of DAGs. But the space of Baye...
Tomás Kocka, Robert Castelo
IJCAI
2003
14 years 25 days ago
Optimal Time-Space Tradeoff in Probabilistic Inference
Recursive Conditioning, RC, is an any-space algorithm lor exact inference in Bayesian networks, which can trade space for time in increments of the size of a floating point number...
David Allen, Adnan Darwiche
IICAI
2003
14 years 25 days ago
Performance Analysis of an Acyclic Genetic approach to Learn Bayesian Network Structure
Abstract. We introduce a new genetic algorithm approach for learning a Bayesian network structure from data. Our method is capable of learning over all node orderings and structure...
Pankaj B. Gupta, Vicki H. Allan
FLAIRS
2003
14 years 25 days ago
Using Bayesian Networks for Cleansing Trauma Data
Medical data is unique due to its large volume, heterogeneity and complexity. This necessitates costly active participation of medical domain experts in the task of cleansing medi...
Prashant Doshi, Lloyd Greenwald, John R. Clarke
FLAIRS
2003
14 years 25 days ago
An Extension of the Differential Approach for Bayesian Network Inference to Dynamic Bayesian Networks
We extend the differential approach to inference in Bayesian networks (BNs) (Darwiche, 2000) to handle specific problems that arise in the context of dynamic Bayesian networks (D...
Boris Brandherm
EUSFLAT
2003
155views Fuzzy Logic» more  EUSFLAT 2003»
14 years 25 days ago
Bayesian networks for transport decision scenarios
Bayesian networks are formal graphical languages for representation and communication of decision scenarios requiring reasoning under uncertainty. We will analyze Bayesian network...
Alexander Holland
EUSFLAT
2003
152views Fuzzy Logic» more  EUSFLAT 2003»
14 years 25 days ago
Bayesian networks for continuous values and uncertainty in the learning process
This paper proposes a method for Bayesian networks that handles uncertainty and discretization of continuous variables when learning the networks from a database of cases. The dat...
J. F. Baldwin, E. Di Tomaso
FLAIRS
2006
14 years 25 days ago
Generating Realistic Large Bayesian Networks by Tiling
In this paper we present an algorithm and software for generating arbitrarily large Bayesian Networks by tiling smaller real-world known networks. The algorithm preserves the stru...
Ioannis Tsamardinos, Alexander R. Statnikov, Laura...
FLAIRS
2004
14 years 25 days ago
An Empirical Study of Probability Elicitation Under Noisy-OR Assumption
Bayesian network is a popular modeling tool for uncertain domains that provides a compact representation of a joint probability distribution among a set of variables. Even though ...
Adam Zagorecki, Marek J. Druzdzel
BIOCOMP
2006
14 years 25 days ago
Learning Genetic and Gene Bayesian Networks with Hidden Variables: Bilayer Verification Algorithm
To improve the recovery of gene-gene and marker-gene (eQTL) interaction networks from microarray and genetic data, we propose a new procedure for learning Bayesian networks. This a...
Jason E. Aten