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IFIP12
2008
13 years 6 months ago
Bayesian Networks Optimization Based on Induction Learning Techniques
Obtaining a bayesian network from data is a learning process that is divided in two steps: structural learning and parametric learning. In this paper, we define an automatic learni...
Paola Britos, Pablo Felgaer, Ramón Garc&iac...
JMLR
2012
11 years 7 months ago
Markov Logic Mixtures of Gaussian Processes: Towards Machines Reading Regression Data
We propose a novel mixtures of Gaussian processes model in which the gating function is interconnected with a probabilistic logical model, in our case Markov logic networks. In th...
Martin Schiegg, Marion Neumann, Kristian Kersting
ATAL
2004
Springer
13 years 10 months ago
A Bayes Net Approach to Argumentation
Argumentation-based negotiation approaches have been proposed to present realistic negotiation contexts. This paper presents a novel Bayesian network based argumentation and decis...
Sabyasachi Saha, Sandip Sen
GI
2004
Springer
13 years 10 months ago
Integrating an Agile Process in a Model Driven Architecture
The model driven development is an interested area among software engineers as well as the agile development. In fact, combining model driven and agile practices is an interesting ...
Paloma Cáceres, Francisco Díaz, Espe...
SAC
2008
ACM
13 years 4 months ago
Bayesian inference for a discretely observed stochastic kinetic model
The ability to infer parameters of gene regulatory networks is emerging as a key problem in systems biology. The biochemical data are intrinsically stochastic and tend to be observ...
Richard J. Boys, Darren J. Wilkinson, Thomas B. L....