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» A geometric view on learning Bayesian network structures
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IJAR
2010
113views more  IJAR 2010»
13 years 8 months ago
A geometric view on learning Bayesian network structures
We recall the basic idea of an algebraic approach to learning Bayesian network (BN) structures, namely to represent every BN structure by a certain (uniquely determined) vector, c...
Milan Studený, Jirí Vomlel, Raymond ...
IJAR
2011
86views more  IJAR 2011»
13 years 1 months ago
On open questions in the geometric approach to structural learning Bayesian nets
The basic idea of an algebraic approach to learning Bayesian network (BN) structures is to represent every BN structure by a certain uniquely determined vector, called the standar...
Milan Studený, Jirí Vomlel
JMLR
2010
149views more  JMLR 2010»
13 years 4 months ago
Learning Bayesian Network Structure using LP Relaxations
We propose to solve the combinatorial problem of finding the highest scoring Bayesian network structure from data. This structure learning problem can be viewed as an inference pr...
Tommi Jaakkola, David Sontag, Amir Globerson, Mari...
NN
1997
Springer
174views Neural Networks» more  NN 1997»
14 years 2 months ago
Learning Dynamic Bayesian Networks
Bayesian networks are directed acyclic graphs that represent dependencies between variables in a probabilistic model. Many time series models, including the hidden Markov models (H...
Zoubin Ghahramani
CORR
2010
Springer
69views Education» more  CORR 2010»
13 years 10 months ago
A Geometric View of Conjugate Priors
In Bayesian machine learning, conjugate priors are popular, mostly due to mathematical convenience. In this paper, we show that there are deeper reasons for choosing a conjugate pr...
Arvind Agarwal, Hal Daumé III