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» The Representational Power of Discrete Bayesian Networks
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AAAI
2000
13 years 9 months ago
Semantics and Inference for Recursive Probability Models
In recent years, there have been several proposals that extend the expressive power of Bayesian networks with that of relational models. These languages open the possibility for t...
Avi Pfeffer, Daphne Koller
FLAIRS
2008
13 years 10 months ago
A First-Order Bayesian Tool for Probabilistic Ontologies
One of the major weaknesses of current research on the Semantic Web (SW) is the lack of proper means to represent and reason with uncertainty. A number of recent efforts from the ...
Paulo Cesar G. da Costa, Marcelo Ladeira, Rommel N...
INFORMS
1998
142views more  INFORMS 1998»
13 years 7 months ago
Distributed State Space Generation of Discrete-State Stochastic Models
High-level formalisms such as stochastic Petri nets can be used to model complex systems. Analysis of logical and numerical properties of these models often requires the generatio...
Gianfranco Ciardo, Joshua Gluckman, David M. Nicol
AI
2000
Springer
13 years 7 months ago
Credal networks
Credal networks are models that extend Bayesian nets to deal with imprecision in probability, and can actually be regarded as sets of Bayesian nets. Evidence suggests that credal ...
Fabio Gagliardi Cozman
RECOMB
2004
Springer
14 years 8 months ago
Learning Regulatory Network Models that Represent Regulator States and Roles
Abstract. We present an approach to inferring probabilistic models of generegulatory networks that is intended to provide a more mechanistic representation of transcriptional regul...
Keith Noto, Mark Craven